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Office: Margaret Jacks Hall, Building 460, Suite 040
Mail Code: 94305-2150
Phone: (650) 498-8720
Email: symsys-afs@lists.stanford.edu
Web Site: http://symsys.stanford.edu

Courses offered by the Symbolic Systems Program are listed under the subject code SYMSYS on the Stanford Bulletin's ExploreCourses web site.

The observation that both human beings and computers can manipulate symbols lies at the heart of Symbolic Systems, an interdisciplinary program focusing on the relationship between natural and artificial systems that represent, process, and act on information. Computer programs, natural languages, the human mind, and the Internet embody concepts whose study forms the core of the Symbolic Systems curriculum, such as computation, representation, communication, and intelligence. A body of knowledge and theory has developed around these notions, from disciplines such as philosophy, computer science, linguistics, psychology, statistics, neurobiology, and communication. Since the invention of computers, researchers have been working across these disciplines to study questions such as: in what ways are computers and computer languages like human beings and their languages; how can the interaction between people and computers be made easier and more beneficial?

The core requirements of the Symbolic Systems Program (SSP) include courses in symbolic logic, the philosophy of mind, formal linguistics, cognitive psychology, programming, the mathematics of computation, statistical theory, artificial intelligence, and interdisciplinary approaches to cognitive science. These courses prepare students with the vocabulary, theoretical background, and technical skills needed for study and research at the advanced undergraduate and graduate levels. Most of the courses in SSP are drawn from affiliated departments. Courses designed specifically for the program are aimed at integrating and supplementing topics covered by the department-based offerings. The curriculum includes humanistic approaches to questions about language and intelligence, as well as training in science and engineering.

SSP offers B.S. and M.S. degree programs. Both programs require students to master a common core of required courses and to choose an area of specialization.

Mission of the Undergraduate Program in Symbolic Systems

The undergraduate program in Symbolic Systems is an interdisciplinary program focusing on the relationships between natural and artificial systems that use symbols to communicate and to represent information. The mission of the program is to prepare majors with the vocabulary, theoretical background, and technical skills necessary to research questions about language, information, and intelligence, both human and machine. The curriculum offers a combination of traditional humanistic approaches to these questions as well as a training and familiarity with contemporary developments in the science and technology of computation. Students in the major take courses in cognitive science, computer programming, logic and computational theory, probability, cognitive psychology, philosophy of mind, linguistics, and artificial intelligence. The program prepares students for a variety of careers in the private and public sectors, especially those involving the human-facing sides of information systems/technology, as well as for further study and research in the cognitive and/or information sciences.

Learning Outcomes (Undergraduate)

The program expects its undergraduate majors to be able to demonstrate the following learning outcomes. These learning outcomes are used in evaluating students and the Symbolic Systems Program. Students are expected to demonstrate:

1. ability to apply formal, philosophical, and/or computational analysis to experimental designs and data and vice versa.

2. ability to understand multiple formal, philosophical, and/or computational frameworks and how they are related to each other.

3. ability to map real world problems or observed phenomena onto formal, philosophical and/or computational frameworks and vice versa.

Learning Outcomes (Graduate)

The purpose of the master's program is to further develop knowledge and skills in Symbolic Systems and to prepare students for a professional career or doctoral studies. This is achieved through completion of courses representing each of the core disciplines of Symbolic Systems as well as an individualized course program in support of the completion of a Master's thesis.

Bachelor of Science in Symbolic Systems

The program offers a Bachelor of Science in Symbolic Systems, as well as an Bachelor of Science with Honors in Symbolic Systems and a Minor in Symbolic Systems. A major in Symbolic Systems qualifies as a Science, Technology, Engineering, and Mathematics (STEM) major under the U.S. Department of Homeland Security's Designated Degree Programs list of STEM programs. Depending on the plan of study, Sym Sys students can be classified as studying Cognitive Science (2010 CIP Code 30.2501) and/or Informatics (2010 CIP Code 11.0104).

Students declaring the major prior to 2020-21 should consult previous Stanford Bulletins for degree requirements. Such students should consult the student services office if they want to change to the new requirements.

How to Declare the Major

To declare a major in Symbolic Systems, a student must:

  • Be enrolled in or have completed SYMSYS 1 Minds and Machines
  • Declare the major in Axess, and have the declaration approved by the program student services officer.
  • Submit a preliminary Course Plan form for the major to a declaration interview with one of the Advising Fellows or with the Associate Director of the Program; see the calendar of Office Hours on the Symsys website for possible interview times.

Advising

Upon declaration approval, students are assigned to both the Program Director and Associate Director as major advisors. The student must also select and confirm a concentration advisor.

  • Declared majors have until the Autumn Quarter of their junior year to select a concentration advisor. Juniors declaring the major must have a concentration advisor confirmed at the time of declaration. 
  • A hold is placed on Winter Quarter registration for juniors who do not have a concentration advisor by Autumn Quarter of their junior year. (See the COVID-19 Policies tab for a one-year extension to Winter Quarter for this requirement.)
  • Any individual with an ongoing instructional appointment at Stanford (listed as such in Chapters 2, 6, or 9 of the Faculty Handbook) may serve as the concentration advisor. To confirm a concentration advisor after an eligible faculty member has agreed to fill this role, student must send an email message to symsys-sso@stanford.edu and the concentration advisor,  including a statement of how the student plans to fulfill the capstone requirement of the major. Changes to capstone plans require the approval of the  concentration advisor.

Degree Requirements

The Symbolic Systems major requires completion of:

  • The core: a common set of foundations, breadth requirements, and experiential requirements that all students in the program must complete
  • An approved concentration: depth in a particular specialization chosen by the student. See a list of Concentrations below.

Students must submit a course plan to the student services officer for Symbolic Systems at least two quarters prior to the planned graduation date, listing courses taken or that will be completed to fulfill the course requirements for the major.

Students must obtain approval for any courses not listed as approved for a major requirement.

All courses taken to fulfill a major requirement for Symbolic Systems must be passed for 3 units or more, with either a letter grade ('C-' or better for core courses, and a 'D-' or above for concentration courses) a no-option pass grade ('S' or its equivalent in the Graduate School of Business, Stanford Law School, or School of Medicine, or in an approved transfer credit course from another institution. A 'CR' cannot be used to fulfill a major requirement for Symbolic Systems), except as modified by the COVID-19 policies in effect during 2020-21. Students who have already completed a required course with a 'CR' grade may file a Replacement Petition to take a course in the same subject area at the same or a higher level in order to avoid having to retake the course. 

Unless otherwise stated, each course that is counted for the major must be taken for 3 units or more. Taking a course for 3 units is sufficient unless the requirement specifically states otherwise.

Each course taken for the major may be counted toward at most one required course in either the Core or Concentration (not both), except in cases where double-counting is explicitly allowed.

Students in a dual degree program, students taking a minor, or students in coterminal program, may not double-count courses towards different degree programs or minors unless a course is an introductory skill requirement for both majors.

The program is open to requests to approving courses not listed as options to fulfill major requirements. Consult the student services office for details of this process.

Core

Core requirements are typically completed earlier than a student's concentration, but the only requirements that impose explicit restrictions on when a course can be completed during a student's undergraduate career are the gateway and capstone requirements.

Course Requirements 

Units
1. Preparations4
These courses should be completed early in the major.
a. Gateway Course
SYMSYS 1Minds and Machines4
b. Single Variable Calculus10
One of the following:
MATH 19, MATH 20, and MATH 21 (or MATH 21A): Calculus
10 units of Advanced Placement Calculus credit
Placement by the Mathematics Placement Diagnostic into MATH 20 or MATH 21 and completion of the rest of the series, or into MATH 51
c. Multivariate Systems3-6
One of the following:
CME 100Vector Calculus for Engineers5
CME 100AVector Calculus for Engineers, ACE6
MATH 51Linear Algebra, Multivariable Calculus, and Modern Applications5
MATH 51ALinear Algebra, Multivariable Calculus, and Modern Applications, ACE6
MATH 61CMModern Mathematics: Continuous Methods5
MATH 61DMModern Mathematics: Discrete Methods5
d. Further Study in Multivariate Systems3-5
Optional, but recommended, and may be used as contingent electives in a concentration. One or more of the following courses, which may be needed as preparation for some Core options and other advanced courses in the major.
CME 102Ordinary Differential Equations for Engineers (and (optionally) CME 104)5
CME 102AOrdinary Differential Equations for Engineers, ACE (, ACE, and (optionally) CME 104A, ACE)6
CME 104Linear Algebra and Partial Differential Equations for Engineers5
ENGR 108Introduction to Matrix Methods (formerly CME 103)3-5
MATH 52Integral Calculus of Several Variables5
MATH 53Ordinary Differential Equations with Linear Algebra5
MATH 62CMModern Mathematics: Continuous Methods5
MATH 62DMModern Mathematics: Discrete Methods5
MATH 63CMModern Mathematics: Continuous Methods5
MATH 104Applied Matrix Theory3
MATH 113Linear Algebra and Matrix Theory3
2. Breadth Requirements9-15
One three quarter sequence of training in each of four methodological areas, plus a Cross-Area Requirement.
a. Philosophical Analysis
i. An introductory course in the Philosophy Department
One of the following:
Any course listed with a PHIL number (with the exception of PHIL 99/SYMSYS 1)
THINK 69Emotion4
ii. Writing in the Major (WIM) course
PHIL 80Mind, Matter, and Meaning5
iii. An advanced undergraduate Philosophy course that lists PHIL 80 as a prerequisite
One of the following:
PHIL 107BPlato's Later Metaphysics and Epistemology4
PHIL 167DPhilosophy of Neuroscience4
PHIL 172History of Modern Moral Philosophy4
PHIL 173BMetaethics4
PHIL 175Philosophy of Law4
PHIL 180Metaphysics4
PHIL 180ARealism, Anti-Realism, Irrealism, Quasi-Realism4
PHIL 181Philosophy of Language4
PHIL 182Advanced Philosophy of Language4
PHIL 182ANaturalizing Representation4
PHIL 182HTruth4
PHIL 184Topics in Epistemology4
PHIL 186Philosophy of Mind4
PHIL 187Philosophy of Action4
PHIL 189GFine-Tuning Arguments for God's Existence4
b. Formal Methods
Courses that focus on rigorous definitions, axioms, theorems, and proofs, and their use in developing mathematical theories and meta-theories. Each of the following:
i. Formal Logic
One of the following:
CS 157Computational Logic3
PHIL 150Mathematical Logic4
PHIL 151Metalogic (Prerequisite: PHIL 150 or instructor permission)4
ii. Theory of Computation. One of the following:
CS 103Mathematical Foundations of Computing (Corequisite: CS 106B or X)3-5
CS 154Introduction to the Theory of Computation (Prerequisite: CS 103 or significant proof-writing experience.)3-4
iii. Probability Theory and Statistics
A course that covers the theory of probability and is grounded in multivariable calculus. One of the following:
CME 106Introduction to Probability and Statistics for Engineers4
CS 109Introduction to Probability for Computer Scientists3-5
EE 178Probabilistic Systems Analysis3-4
MATH 151Introduction to Probability Theory3
MATH 63DMModern Mathematics: Discrete Methods5
MS&E 120Introduction to Probability4
MS&E 220Probabilistic Analysis3-4
STATS 110Statistical Methods in Engineering and the Physical Sciences5
STATS 116Theory of Probability4
c. Computational Methods
Courses that focus on software design, data structures, algorithms, development, applications, evaluation, and simulation. Each of the following:
i. Programming 1
One of the following:
CS 106AProgramming Methodology3-5
Equivalent preparation, as evidenced by successful completion of CS 106B or 106X
ii. Programming II
One of the following:
CS 106BProgramming Abstractions3-5
CS 106XProgramming Abstractions (Accelerated)3-5
iii. A post CS 106B course covering one or more broad computational methods with a programming component.
One of the following:
CS 107Computer Organization and Systems3-5
CS 107EComputer Systems from the Ground Up3-5
CS 129Applied Machine Learning3-4
CS 147Introduction to Human-Computer Interaction Design (Plus one of the following:)3-5
CS 193AAndroid Programming3
CS 193CClient-Side Internet Technologies3
CS 193PiOS Application Development3
CS 193XWeb Programming Fundamentals3
CS 194HUser Interface Design Project3-4
CS 221Artificial Intelligence: Principles and Techniques3-4
CS 229Machine Learning3-4
d. Empirical Cognitive Science
Courses that focus on questions, hypotheses, models, predictions, and explanations that are derived from or testable in neural and behavioral data. Each of the following:
i. Overview of psychology.
PSYCH 1Introduction to Psychology5
ii. An introductory area course in cognition, language, and neuroscience.
One of the following:
BIO 150Human Behavioral Biology5
LINGUIST 145Introduction to Psycholinguistics4
LINGUIST 150Language and Society3-4
PSYCH 30Introduction to Perception4
PSYCH 45Introduction to Learning and Memory3
PSYCH 50Introduction to Cognitive Neuroscience4
PSYCH 60Introduction to Developmental Psychology3
PSYCH 70Self and Society: Introduction to Social Psychology4
PSYCH 75Introduction to Cultural Psychology5
PSYCH 141Cognitive Development3
PSYCH 154Judgment and Decision-Making3
iii. Linguistic Theory
A course introducing a core area of theoretical inquiry in linguistics. One of the following:
LINGUIST 105Phonetics4
LINGUIST 110Introduction to Phonology4
LINGUIST 120Introduction to Syntax4
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 130BIntroduction to Lexical Semantics3-4
Additional approved undergraduate courses offered on a semi-regular basis:
LINGUIST 21NLinguistic Diversity and Universals: The Principles of Language Structure3
LINGUIST 30NLinguistic Meaning and the Law3
LINGUIST 121AThe Syntax of English4
LINGUIST 121BCrosslinguistic Syntax4
LINGUIST 134AThe Structure of Discourse: Theory and Applications2-4
LINGUIST 160Introduction to Language Change2-4
Cross-Area Requirement
A non-introductory course, which has as a prerequisite at least one Core course (or equivalent), and which combines methods and subject matter from at least two Breadth areas in the Core. One of the following:
i. Suggested courses for most students
Only one course must be chosen to fulfill the requirement - categories are for guidance only:
CS 147Introduction to Human-Computer Interaction Design3-5
CS 229Machine Learning3-4
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 180From Languages to Information3-4
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
PHIL 167DPhilosophy of Neuroscience4
PHIL 181Philosophy of Language4
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 209Neural Network Models of Cognition4
ii. Any other course on the full list of courses approved for this requirement below.
3. Experiential Requirements
Each of the following:
a. Advanced Small Seminar Requirement.
An approved course which (a) builds on the Core Preparations and Breadth Requirements, (b) is small -- 20 students or fewer, and (c) is an interactive, discussion-based seminar. May be double-counted for an applicable Concentration requirement, but not for a Core requirement.
b. Capstone
A two-course requirement consisting of the following components, chosen in consultation with and approved by a student's Concentration Advisor (3 or more units each):
i. Practicum
A project or internship-accompanying course. One of the following:
SYMSYS 190Senior Honors Tutorial1-5
An approved project course with a SYMSYS listing in the 195-series. Any of the following:
SYMSYS 195ADesign for Artificial Intelligence3-4
SYMSYS 195BDesign for Behavior Change3-4
SYMSYS 195DResearch in Digital Democracy3-4
SYMSYS 195EExperimental Methods3
SYMSYS 195GIntroduction to Game Design3-4
SYMSYS 195IImage Systems Engineering1-3
SYMSYS 195LMethods in Psycholinguistics4
SYMSYS 195NNatural Language Processing with Deep Learning3-4
SYMSYS 195SService Design3-4
SYMSYS 195UNatural Language Understanding3-4
SYMSYS 195VData Visualization3-4
Supervised Research
Taken with a faculty member on an approved symbolic-systems related project, taken as SYMSYS 196: Independent Study, or a department-based directed research course.
SYMSYS 192: Symbolic Systems in Practice (must be taken in conjunction with an approved internship or service project)
ii. Integrative Requirement
Either an additional research project course (e.g., the second course of an Honors Project) or a Concentration-Specific Integrative Course, which must be completed no earlier than the Junior Year. Units must be applied to a student's concentration.
One of the following (the first three bulleted options are the Standard Options available across all Concentrations):
SYMSYS 190Senior Honors Tutorial (continuation of the course taken for the Practicum requirement)1-5
An approved project course with a SYMSYS listing in the 195-series
(See list under "Practicum" above - may be either the second quarter of a 2-quarter course, or a one-quarter course)
Supervised research with a faculty member on an approved symbolic-systems related project, taken as SYMSYS 196 Independent Study, or a department-based directed research course (may be either the second quarter of a 2-quarter course or a one-quarter course)
An approved Concentration-Specific Integrative Course taken within a Concentration.
Total Units75-90

Full List of Cross-Area Requirement Courses

Units
Cross-Area Requirement
The full list of approved courses for the Cross-Area Requirement.
Only one course must be chosen to fulfill the requirement - categories are for guidance only:
Philosophical Analysis and Formal Methods
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
PHIL 162Philosophy of Mathematics4
PHIL 181Philosophy of Language4
Philosophical Analysis and Computational Methods
CS 181Computers, Ethics, and Public Policy4
CS 182Ethics, Public Policy, and Technological Change5
PHIL 152Computability and Logic4
PHIL 167DPhilosophy of Neuroscience4
Philosophical Analysis and Empirical Cognitive Science
PHIL 167DPhilosophy of Neuroscience4
PHIL 181Philosophy of Language4
PHIL 186Philosophy of Mind4
Formal Methods and Computational Methods
CS 151Logic Programming3
CS 154Introduction to the Theory of Computation3-4
CS 161Design and Analysis of Algorithms3-5
CS 229Machine Learning3-4
CS 238Decision Making under Uncertainty3-4
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 180From Languages to Information3-4
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 209Neural Network Models of Cognition4
PSYCH 221Image Systems Engineering1-3
PSYCH 242Theoretical Neuroscience3
PHIL 249Evidence and Evolution3-5
Formal Methods and Empirical Cognitive Science
PSYCH 253Advanced Statistical Modeling3
CS 229Machine Learning3-4
ECON 178Behavioral Economics5
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 180From Languages to Information3-4
PHIL 154Modal Logic4
PHIL 181Philosophy of Language4
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 209Neural Network Models of Cognition4
PSYCH 221Image Systems Engineering1-3
PSYCH 242Theoretical Neuroscience3
PSYCH 249Large-Scale Neural Network Modeling for Neuroscience1-3
PSYCH 253Advanced Statistical Modeling3
Computational Methods and Empirical Cognitive Science
CS 147Introduction to Human-Computer Interaction Design3-5
CS 229Machine Learning3-4
CS 448BData Visualization3-4
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 180From Languages to Information3-4
PHIL 167DPhilosophy of Neuroscience4
PSYCH 164Brain decoding3
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 209Neural Network Models of Cognition4
PSYCH 221Image Systems Engineering1-3
PSYCH 204AHuman Neuroimaging Methods3
PSYCH 242Theoretical Neuroscience3
PSYCH 249Large-Scale Neural Network Modeling for Neuroscience1-3
PSYCH 253Advanced Statistical Modeling3

Concentration Areas

Please note: the concentrations areas are being revised, and new ones being added.

Applied Logic

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Applied Logic. All courses must be taken for 3 units of more.
Metalogic3-5
Metalogic
Computability3-5
Select one of the following:
Introduction to the Theory of Computation
Computability and Logic
Computational Approaches to Logic3-5
Select one of the following:
Logic Programming
Computational Logic
Set Theory3-5
Set Theory
Integrative Requirement. Must be completed no earlier than the Junior Year.3-5
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Logic Programming
The Practice of Theory Research
Computational Law
General Game Playing
Probabilistic Graphical Models: Principles and Techniques
Programming Languages
Computational Complexity
Programming Language Foundations
Introduction to Semantics and Pragmatics
Advanced Semantics
Modal Logic
Philosophy of Mathematics
Formal Epistemology
Measurement Theory
Logic and Artificial Intelligence
Topics in Logic, Information and Agency
Computation and Cognition: The Probabilistic Approach
What makes a good explanation? Psychological and philosophical perspectives
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Computational Complexity II
Advanced Topics in Semantics & Pragmatics
Proofs and Modern Mathematics
Additional courses may be added here in the future.
Total Units15-25

Artificial Intelligence

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-21 must complete the following requirements to qualify for a Concentration in Artificial Intelligence. All courses must be taken for 3 units of more.
Students in this Concentration are urged to take CS 161, either for the Core Cross-Area Requirement, or as a Contingent Elective, and prior to taking CS 221.
Programming3-5
Select one of the following:
Computer Organization and Systems
Computer Systems from the Ground Up
Introduction3-5
Artificial Intelligence: Principles and Techniques
Artificial Intelligence Depth3-5
Two courses chosen from the "select" list of AI courses (category B of the MSCS AI Track):
Introduction to Robotics
Natural Language Processing with Deep Learning
Spoken Language Processing
Natural Language Understanding
Machine Learning with Graphs
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Computer Vision: From 3D Reconstruction to Recognition
Convolutional Neural Networks for Visual Recognition
Reinforcement Learning
Decision Making under Uncertainty
Integrative Requirement3-5
Must be completed no earlier than the Junior Year
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Language and Technology
Advanced Topics in Human Virtual Representation
Computer Vision: Foundations and Applications
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Machine Learning Theory
Data for Sustainable Development
Machine Learning Under Distributional Shifts
Computational Models of the Neocortex
From Languages to Information
Research Seminar in Computer-Generated Music
Computational Neuroscience
Logic and Artificial Intelligence
Topics in Logic, Information and Agency
Brain decoding
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Theoretical Neuroscience
Topics in Natural and Artificial Intelligence
Large-Scale Neural Network Modeling for Neuroscience
What makes a good explanation? Psychological and philosophical perspectives
Machine Learning Methods for Neural Data Analysis
Theories of Consciousness
Contingent Electives3-5
If any of requirements 1-3 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Modeling Biomedical Systems
Representations and Algorithms for Computational Molecular Biology
Hardware Accelerators for Machine Learning
General Game Playing
Deep Generative Models
Mining Massive Data Sets
Deep Multi-task and Meta Learning
Computer Graphics in the Era of AI
Visual Computing Systems
Regulating Artificial Intelligence
Networks
Data Privacy and Ethics
Compositional Algorithms, Psychoacoustics, and Computational Music
Research Seminar in Computer-Generated Music
Philosophy of Artificial Intelligence
Introduction to Statistical Inference
Data Mining and Analysis
Modern Applied Statistics: Learning
Modern Applied Statistics: Data Mining
Total Units15-25

Biomedical Applications

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Biomedical Applications. All courses must be taken for 3 units of more.
Philosophical and Ethical Inquiry3-5
For example, any of the following:
Foundations of Bioethics
Intro to Disability Studies: Disability and Technology
Social and Ethical Issues in the Neurosciences
Topics in Philosophy of Medicine
Phenomenology: Animals
Philosophy of Neuroscience
Biological Individuality
Introduction to Environmental Ethics
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Theories of Consciousness
The Philosophy and Science of Perception
Theoretical and Mathematical Approaches3-5
For example, any of the following:
Theoretical Population Genetics
Stochastic and Nonlinear Dynamics
Quantitative Evolutionary Dynamics and Genomics
Topics in Biomedical Data Science: Large-scale inference
Mathematical Models and Medical Decisions
Market Design
Signal Processing and Linear Systems I
Introduction to Statistics for the Health Sciences
Introduction to Health Sciences Statistics
Principles of Epidemiology
Health Policy Modeling
Biostatistics
Statistical Models in Biology
Computational and Design Methods3-5
For example, any of the following:
Artificial Intelligence in Healthcare
Neuromorphics: Brains in Silicon
Modeling Biomedical Systems
Computational Methods for Biomedical Image Analysis and Interpretation
Deep Learning in Genomics and Biomedicine
Computational Biology: Structure and Organization of Biomolecules and Cells
Design for Behavior Change
Service Design
The Human Genome Source Code
Artificial Intelligence for Disease Diagnosis and Information Recommendations
Computational Models of the Neocortex
Data Visualization
Genomics
Big Data for Biologists - Decoding Genomic Function
Biology, Health and Big Data
Topics in Neurodiversity: Design Thinking Approaches
Computational Neuroimaging
Machine Learning Methods for Neural Data Analysis
Cognition in Interaction Design
Experimental and Observational Science
For example, any of the following:
Introduction to Ecology
Genetics
Physiology
Human Behavioral Biology
Mechanisms of Neuron Death
Neuroplasticity: From Synapses to Behavior
Topics in Biomedical Data Science: Large-scale inference
Big Data for Biologists - Decoding Genomic Function
Biology, Health and Big Data
Principles of Epidemiology
Health Policy Modeling
The Nervous System
Brain Plasticity
Introduction to Perception
Introduction to Learning and Memory
Introduction to Cognitive Neuroscience
Introduction to Developmental Psychology
Ion Transport and Intracellular Messengers
Brain Networks
Advanced Seminar on Memory
Cognitive Neuroscience
Human Neuroimaging Methods
Brain and Decision
Affective Neuroscience
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Modeling Biomedical Systems
Artificial Intelligence in Healthcare
Computational Methods for Biomedical Image Analysis and Interpretation
The Human Genome Source Code
Deep Learning in Genomics and Biomedicine
Computational Biology: Structure and Organization of Biomolecules and Cells
Advanced Topics in Human Virtual Representation
Data for Sustainable Development
Artificial Intelligence for Disease Diagnosis and Information Recommendations
Computational Models of the Neocortex
Philosophy of Neuroscience
Biological Individuality
Introduction to Environmental Ethics
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Topics in Neurodiversity: Design Thinking Approaches
Ion Transport and Intracellular Messengers
Brain Networks
Advanced Seminar on Memory
Cognitive Neuroscience
Human Neuroimaging Methods
Computational Neuroimaging
Brain and Decision
Affective Neuroscience
Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement
Machine Learning Methods for Neural Data Analysis
Cognition in Interaction Design
Contingent Electives
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Additional courses may be added here in the future.
Total Units15-25

Cognitive Science

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Cognitive Science. All courses must be taken for 3 units of more.
Cognitive Neuroscience3-5
Select one of the following:
Introduction to Perception
Introduction to Learning and Memory
Introduction to Cognitive Neuroscience
Inferential Statistics3-5
Select one of the following:
Data Analysis for Quantitative Research
Introduction to Applied Statistics
Fundamentals of Data Science: Prediction, Inference, Causality
Introduction to Statistical Methods: Precalculus
Advanced Statistical Modeling
Introduction to Data Analysis
Data Science 101
Statistical Methods in Engineering and the Physical Sciences
Introduction to Applied Statistics
Introduction to Statistical Inference
Data Mining and Analysis
Research Methods3-5
A course on research practices and/or methods that are commonly used for studying cognition, language, and the brain. For example, one of the following:
Computer Organization and Systems
Applied Machine Learning
Machine Learning
From Languages to Information
Natural Language Understanding
Methods in Psycholinguistics
Philosophy of Neuroscience
Brain decoding
Research Methods in Cognition & Development
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Image Systems Engineering
Curiosity in Artificial Intelligence
Theoretical Neuroscience
Large-Scale Neural Network Modeling for Neuroscience
Experimental Methods
Advanced Statistical Modeling
Measurement and the Study of Change in Social Science Research
Machine Learning Methods for Neural Data Analysis
Cognitive Science Depth3-5
For example, one of the following courses:
Human Behavioral Biology
Media Processes and Effects
Advanced Studies in Behavior and Social Media
Computer Vision: Foundations and Applications
Introduction to the Theory of Computation
Natural Language Processing with Deep Learning
General Game Playing
Probabilistic Graphical Models: Principles and Techniques
Machine Learning Theory
Computer Vision: From 3D Reconstruction to Recognition
Reinforcement Learning
Decision Making under Uncertainty
Game Theory and Economic Applications
Educational Neuroscience
Cognitive Development in Childhood and Adolescence
Phonetics
Introduction to Phonology
Learning to Speak: An Introduction to Child Language Acquisition
From Languages to Information
Natural Language Understanding
Seminar in Semantics: Conditionals
Seminar in Developmental Psycholinguistics
Psychophysics and Music Cognition
The Nervous System
Information and Signaling Mechanisms in Neurons and Circuits
Philosophy of Cognitive Science
Computability and Logic
Computing Machines and Intelligence
Modal Logic
Philosophy of Neuroscience
Philosophy of Language
Topics in Epistemology
Formal Epistemology
Philosophy of Mind
Philosophy of Action
Rationality Over Time
Capstone Seminar: Artificial Intelligence
Measurement Theory
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Advanced Topics in Philosophy of Language
Truth as the aim of belief and inquiry
Introduction to Perception
Introduction to Learning and Memory
Introduction to Cognitive Neuroscience
Self and Society: Introduction to Social Psychology
Introduction to Cultural Psychology
Introduction to Psycholinguistics
Cognitive Development
Judgment and Decision-Making
Seminar on Emotion
Brain Networks
Brain decoding
Advanced Seminar on Memory
Social Cognition and Learning in Early Childhood
Cognitive Neuroscience
Computation and Cognition: The Probabilistic Approach
Human Neuroimaging Methods
Computational Neuroimaging
Foundations of Cognition
Neural Network Models of Cognition
Image Systems Engineering
Brain and Decision
Large-Scale Neural Network Modeling for Neuroscience
High-level Vision: From Neurons to Deep Neural Networks
Affective Neuroscience
Current Debates in Learning and Memory
Graduate Seminar on Theory of Mind
Brain Machine Interfaces: Science, Technology, and Application
What makes a good explanation? Psychological and philosophical perspectives
Cognitive Science Perspectives on Humanity and Well-Being
Conceptual Issues in Cognitive Science
Computer Machines and Intelligence
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Advanced Topics in Human Virtual Representation
Computer Vision: Foundations and Applications
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Artificial Intelligence: Principles and Techniques
General Game Playing
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Deep Learning
Computer Vision: From 3D Reconstruction to Recognition
Reinforcement Learning
Decision Making under Uncertainty
Data for Sustainable Development
Computational Models of the Neocortex
Introduction to Machine Learning
From Languages to Information
Research Seminar in Computer-Generated Music
Neuroplasticity and Musical Gaming
Social and Ethical Issues in the Neurosciences
Phenomenology: Animals
Logic and Artificial Intelligence
Research Seminar on Logic and Cognition
Topics in Logic, Information and Agency
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Brain decoding
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Theoretical Neuroscience
Topics in Natural and Artificial Intelligence
Large-Scale Neural Network Modeling for Neuroscience
What makes a good explanation? Psychological and philosophical perspectives
Machine Learning Methods for Neural Data Analysis
Theories of Consciousness
The Philosophy and Science of Perception
Conceptual Issues in Cognitive Science
Computer Machines and Intelligence
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Additional courses may be added here in the future.
Total Units15-25

Computational Foundations

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Computational Foundations. All courses must be taken for 3 units of more.
Students in this Concentration are strongly encouraged to take either CS 181 or CS 182 as part of the major, either for the Core Cross-Area Requirement, for the Capstone Integrative Requirement, as a Contingent Elective, or (in the case of CS 182) for the Core Introductory Philosophy requirement.
Computer Systems I3-5
Select one of the following:
Computer Organization and Systems
Computer Systems from the Ground Up
Computer Systems II3-5
Select one of the following:
Principles of Computer Systems
Operating Systems Principles
Theory of Computation Depth3-5
Select one of the following:
Introduction to the Theory of Computation
Modal Logic
Algorithms3-5
Design and Analysis of Algorithms
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Logic Programming
Computational Logic
The Practice of Theory Research
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Project Lab: Video and Audio Technology for Live Theater in the Age of COVID
Computational Models of the Neocortex
Modal Logic
Topics in Logic, Information and Agency
Computation and Cognition: The Probabilistic Approach
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Any course of 3 units or more, listed with a CS course number greater than 110, excluding CS 196 or CS 198.
Any course of 3 units or more, listed with an EE course number.
Any course of 3 units or more, listed with a MATH course number.
Any course of 3 units or more, listed with a STATS course number.
Philosophy of Artificial Intelligence
Total Units15-25

Computational Social Science Concentration

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Computational Social Science. All courses must be taken for 3 units of more.
Social Behavior3-5
An introductory course in a broad area of social science. Select one of the following:
Ecology for Everyone
Introduction to Ecology
Introduction to Communication
Principles of Economics
Networks and Human Behavior
Economic Analysis I
Game Theory and Economic Applications
Behavioral Economics
Honors Game Theory
Language and Society
Networks
Organizations: Theory and Management
Introduction to Game Theory
The Science of Politics
American Political Institutions in Uncertain Times
Self and Society: Introduction to Social Psychology
Judgment and Decision-Making
Introduction to Sociology
Introduction to Social Networks
Education and Society
Introduction to Comparative Studies in Race and Ethnicity
Statistical Interference 3-5
An introductory course in statistical methods. Select one of the following:
Introduction to Statistical Methods (Postcalculus) for Social Scientists
Introduction to Applied Statistics
Fundamentals of Data Science: Prediction, Inference, Causality
Introduction to Data Analysis
Statistical Methods in Engineering and the Physical Sciences
Introduction to Applied Statistics
Introduction to Statistical Inference
Data Mining and Analysis
Computational Data Methods3-5
A course in machine learning, natural language processing, and/or probabilistic computational inference. Select one of the following:
Applied Machine Learning
Natural Language Processing with Deep Learning
Machine Learning with Graphs
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Deep Learning
Decision Making under Uncertainty
Mining Massive Data Sets
Data Visualization
Applied Econometrics
From Languages to Information
Natural Language Understanding
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Introduction to Statistical Learning
Social Data Science3-5
A course on applying statistical and computational methods to the study of social behavior. Select one of the following:
Communication Research Methods
Data Challenge Lab
Econometric Methods for Public Policy Analysis and Business Decision-Making
Tackling Big Questions Using Social Data Science
Introduction to Data Science
Introduction to Computational Social Science
Data Science for Politics
Causal Inference for Social Science
Natural Language Processing & Text-Based Machine Learning in the Social Sciences
Foundations of Social Research
Computational Undergraduate Research
Social Network Methods
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Advanced Studies in Behavior and Social Media
Advanced Topics in Human Virtual Representation
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Exploring Computational Journalism
Machine Learning with Graphs
Mining Massive Data Sets
Social Computing
Data for Sustainable Development
Game Theory and Economic Applications
Behavioral Economics
Honors Game Theory
Data Privacy and Ethics
Justice
20th Century Political Theory: Liberalism and its Critics
Topics in Logic, Information and Agency
Judgment and Decision-Making
Measurement and the Study of Change in Social Science Research
Natural Language Processing & Text-Based Machine Learning in the Social Sciences
What makes a good explanation? Psychological and philosophical perspectives
The Politics of Algorithms
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Data Analysis for Quantitative Research
Thinking Technology: Anthropological Perspectives
Science as a Creative Process
Evolution
Ecology and Evolution of Animal Behavior
Communication Research Methods
Censorship and Propaganda
Data Challenge Lab
Advanced Digital Media Journalism
Big Local Journalism: a project-based class
Programming in Journalism
Building News Applications
Data Management and Data Systems
Topics in Advanced Robotic Manipulation
Applied Econometrics
Advanced Topics in Econometrics
World Food Economy
Development Economics
Economic Development, Microfinance, and Social Networks
Family and Society
Economic Policy Analysis
Environmental Economics and Policy
Advanced Statistical Methods for Observational Studies
Language, Gender, & Sexuality
Sociophonetics
The Structure of Discourse: Theory and Applications
Sociolinguistic Theory and Analysis
Analysis of Variation
Programming for Linguists
Spoken Language Processing
Machine Learning and Causal Inference
Introduction to Stochastic Modeling
Introduction to Applied Statistics
Future of Work: Issues in Organizational Learning and Design
Dynamic Systems
Stochastic Modeling
Simulation
Incentives and Algorithms
Introduction to Computational Social Science
Data Privacy and Ethics
Energy and Environmental Policy Analysis
Organizational Behavior: Evidence in Action
Health Policy Modeling
Introduction to Moral Philosophy
Introduction to Philosophy of Science
Ethical Theory
Justice
20th Century Political Theory: Liberalism and its Critics
Universal Basic Income: the philosophy behind the proposal
Philosophy of Public Policy
The Science of Politics
What's Wrong with American Government? An Institutional Approach
Data Science for Politics
Causal Inference for Social Science
Public Opinion and American Democracy
Money in Politics
Political Economy of Development
Spatial Approaches to Social Science
Psychology of Xenophobia
Neuroforecasting
Introduction to Sociology
America: Unequal
Introduction to Computational Social Science
Inequality in American Society
Social Networks
Economic Sociology
Social Movements and Collective Action
Gender and Technology
Education and Society
Globalization and Social Change
Justice + Poverty Innovation:Create new solutions for people to navigate housing, medical, & debt
Global Organizations: The Matrix of Change
Crime and Punishment in America
Data Science 101
Introduction to Applied Statistics
Introduction to Statistical Inference
Data Mining and Analysis
Introduction to Regression Models and Analysis of Variance
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
Doing STS: Introduction to Research
Digital Technology, Society, and Democracy
Total Units15-25

Computer Music

See also the Symbolic Systems website.

Symbolic Systems majors must complete the following requirements in addition to the Core requirements to fulfill the Concentration in Computer Music. All courses must be taken for 3 units of more.
Computer-Generated Music I3-5
Fundamentals of Computer-Generated Sound
Computer-Generated Music II3-5
Compositional Algorithms, Psychoacoustics, and Computational Music
Music and the Mind & Brain3-5
Select one of the following:
Music, Mind, and Human Behavior
Psychophysics and Music Cognition
Seminar in Music Perception and Cognition I
Introduction to Perception
Introduction to Cognitive Neuroscience
Integrative Requirement3-5
Must be completed no earlier than the Junior Year
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Stanford Laptop Orchestra: Composition, Coding, and Performance
Research Seminar in Computer-Generated Music
Physical Interaction Design for Music
Psychophysics and Music Cognition
Symbolic Musical Information
Computational Music Analysis
Music, Computing, Design: The Art of Design
Neuroplasticity and Musical Gaming
Seminar in Music Perception and Cognition I
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Object-Oriented Systems Design
Phonetics
Introduction to Phonology
Music, Mind, and Human Behavior
Sound in Space
Total Units15-25

Decision Making and Rationality (DMAR)

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Decision Making and Rationality. All courses must be taken for 3 units of more.
Philosophical Inquiry3-5
Select one of the following:
Data Privacy and Ethics
The Ethical Analyst
Central Topics in the Philosophy of Science: Theory and Evidence
Probability: Ten Great Ideas About Chance
Evolution of the Social Contract
Ethical Theory
Justice
History of Modern Moral Philosophy
Topics in Epistemology
Formal Epistemology
Philosophy of Action
Topics in Logic, Information and Agency
Topics in Normativity
Modern Political Thought: Machiavelli to Marx and Mill
Classical Seminar: Origins of Political Thought
Seminar on Emotion
Formal Decision Theories3-5
Select one of the following:
Economic Analysis II
Market Design
Game Theory and Economic Applications
Honors Game Theory
Advanced Topics in Game Theory and Information Economics
Introduction to Game Theory
Introduction to Game Theory
Modal Logic
Representation Theorems
Formal Methods in Ethics
Measurement Theory
Topics in Logic, Information and Agency
Formal Theory I: Game Theory for Political Science
Microeconomics for Policy
Empirical Findings and Explanations3-5
Select one of the following:
Human Behavioral Biology
Behavioral Economics
Experimental Economics
Behavioral and Experimental Economics II
Seminar on Organizational Theory
Behavioral Economics and the Psychology of Decision Making
Economic Analysis of Political Institutions
Institutions and Bridge-Building in Political Economy
Judgment and Decision-Making
Seminar on Emotion
Foundations of Cognition
Classic and contemporary social psychology research
Mind, Culture, and Society
Social Norms
Brain and Decision
Affective Neuroscience
Economic Sociology
Topics in Economic Sociology
Introduction to Social Networks
Cognitive Science Perspectives on Humanity and Well-Being
Methods and Applications3-5
A course on methods that can be used to study decision making and rationality, or ways to apply research in decision sciences. For example, one of the following:
Outcomes Analysis
Decision Analysis for Civil and Environmental Engineers
Communication Research Methods
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Probabilistic Graphical Models: Principles and Techniques
Reinforcement Learning
Decision Making under Uncertainty
Advanced Topics in Sequential Decision Making
Optimization and Algorithmic Paradigms
Data for Sustainable Development
Economic Analysis I
Applied Econometrics
Advanced Topics in Econometrics
Foundations of Finance
Market Design
Decision Modeling and Information
Public Finance and Fiscal Policy
Economic Policy Analysis
Environmental Economics and Policy
Games Developing Nations Play
Moral and Character Education
Introduction to Optimization
Introduction to Stochastic Modeling
Introduction to Decision Analysis
Organizations: Theory and Management
Introduction to Computational Social Science
Engineering Risk Analysis
Project Course in Engineering Risk Analysis
Introduction to Stochastic Control with Applications
Decision Analysis I: Foundations of Decision Analysis
Security and Risk in Computer Networks
Decision Analysis II: Professional Decision Analysis
Decision Analysis III: Frontiers of Decision Analysis
Influence Diagrams and Probabilistics Networks
Survey of Formal Methods
Thinking Strategically
Introduction to Statistical Methods: Precalculus
Experimental Methods
Advanced Statistical Modeling
Introduction to Applied Statistics
Introduction to Statistical Inference
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
Introduction to Stochastic Processes I
Introduction to Stochastic Processes II
Design of Experiments
Theory of Probability I
Theory of Probability II
Theory of Probability III
Design for Behavior Change
Research in Digital Democracy
Digital Technology, Society, and Democracy
Concepts and Analytic Skills for the Social Sector
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Probabilistic Graphical Models: Principles and Techniques
Reinforcement Learning
Decision Making under Uncertainty
Advanced Topics in Sequential Decision Making
Optimization and Algorithmic Paradigms
Data for Sustainable Development
Topics in Epistemology
Formal Epistemology
Philosophy of Action
Topics in Logic, Information and Agency
Judgment and Decision-Making
Seminar on Emotion
Social Norms
Brain and Decision
Affective Neuroscience
Digital Technology, Society, and Democracy
Cognitive Science Perspectives on Humanity and Well-Being
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Econometric Methods for Public Policy Analysis and Business Decision-Making
Tackling Big Questions Using Social Data Science
How We Decide: Social Choice in the Age of Algorithms
Additional courses may be added here in the future.
Total Units15-25

Human-Centered Artificial Intelligence Concentration

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Human-Centered Artificial Intelligence. All courses must be taken for 3 units of more.
Digital Technology Ethics and Policy3-5
Select one of the following:
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Human Impact3-5
One course aimed at understanding how AI interacts with humans as well as with vital social structures and institutions. For example:
Funkentelechy: Technologies, Social Justice and Black Vernacular Cultures
Thinking Technology: Anthropological Perspectives
Whose Ghost in the Machine? Cultures, Politics and Morals of Artificial Intelligence
The Rise of Digital Culture
Truth, Trust, and Tech
Personality and Digital Media
The Politics of Algorithms
Media Psychology
Race and Media
Advanced Studies in Behavior and Social Media
Law, Order, & Algorithms
A.I.-Activism-Art
Politics of Data: Algorithmic Culture, Big Data, and Information Waste
Regulating Artificial Intelligence
Digital Technology and Law: Foundations
AI and Rule of Law: A Global Perspective
Future of Work: Issues in Organizational Learning and Design
Data Privacy and Ethics
Social and Ethical Issues in the Neurosciences
Universal Basic Income: the philosophy behind the proposal
Gender and Technology
The Public Life of Science and Technology
Digital Technology, Society, and Democracy
Augmenting Human Capabilities3-5
One course aimed at developing new human-centered design methods and tools so that AI agents and applications are designed and created with the ability to communicate with, collaborate with, and augment people more effectively, and to make their work better and more enjoyable. For example:
Artificial Intelligence in Healthcare
Virtual People
Big Local Journalism: a project-based class
Advanced Topics in Human Virtual Representation
Introduction to Human-Computer Interaction Design
Trust and Safety Engineering
Bridging Policy and Tech Through Design
Design for Artificial Intelligence
Design for Behavior Change
Design for Understanding
Service Design
Social Computing
Data for Sustainable Development
Fair, Accountable, and Transparent (FAccT) Deep Learning
Artificial Intelligence for Disease Diagnosis and Information Recommendations
Data Visualization
Market Design
Beyond Bits and Atoms - Lab
Beyond Bits and Atoms: Designing Technological Tools
Educational Neuroscience
Technology for Learners
Behavior Design
Designing AI to Cultivate Human Well-Being
Body Hacking: Applied Topics in Exercise Physiology
Biology, Health and Big Data
Research Seminar in Computer-Generated Music
Brain Plasticity
Topics in Neurodiversity: Design Thinking Approaches
Designing for the 2 Billion: Leading Innovation in Mental Health
Neuroforecasting
Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement
Natural Language Processing & Text-Based Machine Learning in the Social Sciences
Justice + Poverty Innovation:Create new solutions for people to navigate housing, medical, & debt
Cognition in Interaction Design
Intelligence3-5
One course aimed at developing machine intelligence that understands human language, emotions, intentions, behaviors, and interactions at multiple scales. One of the following:
Applied Machine Learning
Computer Vision: Foundations and Applications
Artificial Intelligence: Principles and Techniques
Introduction to Robotics
Natural Language Processing with Deep Learning
Machine Learning
Deep Learning
Natural Language Understanding
Spoken Language Processing
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Virtual People
Media Psychology
Exploring Computational Journalism
Artificial Intelligence: Principles and Techniques
Introduction to Robotics
Machine Learning
Deep Learning
Decision Making under Uncertainty
Design for Understanding
Social Computing
Data for Sustainable Development
Fair, Accountable, and Transparent (FAccT) Deep Learning
Artificial Intelligence for Disease Diagnosis and Information Recommendations
Computational Models of the Neocortex
Curiosity in Artificial Intelligence
Educational Neuroscience
Technology for Learners
From Languages to Information
Spoken Language Processing
Philosophy of Neuroscience
Biological Individuality
Topics in Logic, Information and Agency
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Advanced Topics in Philosophy of Language
Truth as the aim of belief and inquiry
Brain Plasticity
Topics in Neurodiversity: Design Thinking Approaches
Designing for the 2 Billion: Leading Innovation in Mental Health
Ion Transport and Intracellular Messengers
Seminar on Infant Development
Judgment and Decision-Making
Brain Networks
Brain decoding
Advanced Seminar on Memory
Cognitive Neuroscience
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Brain and Decision
Theoretical Neuroscience
Topics in Natural and Artificial Intelligence
Large-Scale Neural Network Modeling for Neuroscience
Affective Neuroscience
Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement
What makes a good explanation? Psychological and philosophical perspectives
Introduction to Statistical Learning
Machine Learning Methods for Neural Data Analysis
Modern Applied Statistics: Data Mining
Theories of Consciousness
The Philosophy and Science of Perception
Cognition in Interaction Design
Contingent Electives
If requirements 1-4 are fulfilled partly from courses taken for Core requirements, then additional approved Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Philosophy of Artificial Intelligence
Introduction to Applied Statistics
Introduction to Statistical Inference
Total Units15-25

Human-Computer Interaction

See also the Symbolic Systems website.

Symbolic Systems majors must complete the following requirements in addition to the Core requirements to fulfill the Concentration in Human-Computer Interaction. All courses must be taken for 3 units of more.
Students in this Concentration are urged to take CS 107 or CS 107E, either for the Post-CS 106B Computation Core requirement, or as a Contingent Elective, and prior to completing requirement 4 below.
Introduction to HCI3-5
Introduction to Human-Computer Interaction Design
Design Methods3-5
Post-CS 147 courses teaching fundamentals of the human-centered design process, featuring a major project component (including any course in the CS 247 series). One of the following:
User Interface Design Project
Design for Artificial Intelligence
Design for Behavior Change
Introduction to Game Design
Service Design
HCI Theory3-5
Courses teaching design, behavioral, and critical theories that underlie the design process. One of the following:
Personality and Digital Media
Virtual People
Media Psychology
Human-Computer Interaction: Foundations and Frontiers
Design Experiments
User Interface Implementation3-5
An advanced course in programming for user interfaces. One of the following:
Object-Oriented Systems Design
Web Applications
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
The Rise of Digital Culture
Personality and Digital Media
Virtual People
Media Psychology
Advanced Studies in Behavior and Social Media
Language and Technology
Advanced Topics in Human Virtual Representation
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Exploring Computational Journalism
Design for Understanding
Social Computing
Human-Computer Interaction: Foundations and Frontiers
Designing Solutions to Global Grand Challenges
Designing Serious Games
Designing for Accessibility
Understanding Users
Learning Experience Design
Technology for Learners
Behavior Design
Child Development and New Technologies
Engineering Education and Online Learning
Product Design Methods
Design Experiments
Digital Technology, Society, and Democracy
Cognition in Interaction Design
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Any d.school course worth 3 or more units
Interactive Art: Making it with Arduino
Mixed-Media Drawing: Art & Aesthetics of Social Media
Intro to Digital / Physical Design
Data as Material
Digital Art I
Introduction to Communication
Media, Culture, and Society
Communication Research Methods
Truth, Trust, and Tech
The Politics of Algorithms
Digital Civil Society
Digital Civil Society
Digital Civil Society
Ethnographic Methods
Race and Gender in Silicon Valley
Introduction to Survey Research
Introduction to Data Science
Data Challenge Lab
Qualitative Research Methodology
Visual Thinking
Designing for Impact
Introduction to Human Values in Design
Design and Manufacturing
Introduction to Mechatronics
Advanced Product Design: Needfinding
Methods in Community Assessment, Evaluation, and Research
Biodesign Fundamentals
Introduction to Applied Statistics
Networks
Data Privacy and Ethics
Introduction to Aesthetics
Introduction to Statistical Methods: Precalculus
Justice + Poverty Innovation:Create new solutions for people to navigate housing, medical, & debt
Data Science 101
Introduction to Applied Statistics
Introduction to Statistical Inference
Data Mining and Analysis
Introduction to Regression Models and Analysis of Variance
Design of Experiments
The Public Life of Science and Technology
Total Units15-25

Learning

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Learning. All courses must be taken for 3 units of more.
Students in the Learning Concentration must complete four courses from areas 1-3 below with at least one from each area, plus one course from area 4. If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses (see area 5) must be completed to total 5 courses beyond those that are taken for the Core.
Computational Learning3-5
Continuous Mathematical Methods with an Emphasis on Machine Learning
Artificial Intelligence: Principles and Techniques
Natural Language Processing with Deep Learning
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Machine Learning Theory
Deep Learning
Reinforcement Learning
Deep Generative Models
Data for Sustainable Development
Introduction to Machine Learning
From Languages to Information
Data Privacy and Ethics
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Large-Scale Neural Network Modeling for Neuroscience
Data Science 101
Machine Learning Methods for Neural Data Analysis
Modern Applied Statistics: Learning
Modern Applied Statistics: Data Mining
Human Learning3-5
Introduction to Teaching and Learning
How to Learn Mathematics
Topics in Cognition and Learning: Technology and Multitasking
Educational Neuroscience
Cognitive Development in Childhood and Adolescence
Social and Emotional Learning: Conceptual & Measurement Issues
Learning to Speak: An Introduction to Child Language Acquisition
Introduction to Learning and Memory
Introduction to Cognitive Neuroscience
Introduction to Developmental Psychology
Cognitive Development
Advanced Seminar on Memory
Social Cognition and Learning in Early Childhood
Cognitive Neuroscience
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Large-Scale Neural Network Modeling for Neuroscience
Experimental Methods
Social Psychology and Social Change
Current Debates in Learning and Memory
Learning Environment Design3-5
Advanced Studies in Behavior and Social Media
Introduction to Human-Computer Interaction Design
User Interface Design Project
Beyond Bits and Atoms - Lab
Learning Experience Design
Beyond Bits and Atoms: Designing Technological Tools
Technology for Learners
Seminar on Teaching Introductory Computer Science
Designing Learning Spaces
Topics in Learning and Technology: Core Mechanics for Learning
Understanding Learning Environments
Child Development and New Technologies
Engineering Education and Online Learning
Unleashing Personal Potential: Behavioral Science and Design Thinking Applied to Self
Neuroplasticity and Musical Gaming
Brain Machine Interfaces: Science, Technology, and Application
Cognition in Interaction Design
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Advanced Topics in Human Virtual Representation
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Artificial Intelligence: Principles and Techniques
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Machine Learning Theory
Deep Learning
Computer Vision: From 3D Reconstruction to Recognition
Reinforcement Learning
Computational Models of the Neocortex
Topics in Epistemology and Education
Curriculum and Instruction Elective in Data Science
Introduction to Machine Learning
From Languages to Information
Formal Epistemology
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Theoretical Neuroscience
Topics in Natural and Artificial Intelligence
Large-Scale Neural Network Modeling for Neuroscience
Measurement and the Study of Change in Social Science Research
Changing Mindsets and Contexts: How to Create Authentic, Lasting Improvement
What makes a good explanation? Psychological and philosophical perspectives
Machine Learning Methods for Neural Data Analysis
Cognition in Interaction Design
Contingent Electives3-5
If any of requirements 1-3 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Introduction to Statistical Methods: Precalculus
Introduction to Applied Statistics
Introduction to Statistical Inference
Additional courses may be added here in the future.
Total Units15-25

Mathematical Foundations

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Mathematical Foundations. All courses must be taken for 3 units of more.
Multivariate Calculus and Linear Algebra10
One of the following two-course sequences (Note: The earlier courses in each series are included in the Core Preparations requirements. Students in this Concentration who began in the CME 100 series should switch to the MATH 52-MATH 53 series for the Concentration.)
Matrix Theory and Applications3-5
Select one of the following:
Continuous Mathematical Methods with an Emphasis on Machine Learning
Linear Algebra and Matrix Theory
Applied Mathematics and Statistics3-5
Select one of the following:
Introduction to Machine Learning
Introduction to Linear Dynamical Systems
Machine Learning Theory
Introduction to Linear Dynamical Systems
Information Theory
Introduction to Combinatorics and Its Applications
Applied Number Theory and Field Theory
Stochastic Processes
Basic Probability and Stochastic Processes with Engineering Applications
Discrete Probabilistic Methods
Introduction to Optimization
Introduction to Optimization (Accelerated)
Introduction to Stochastic Modeling
Dynamic Systems
Introduction to Optimization Theory
Stochastic Modeling
Advanced Statistical Modeling
Statistical Methods in Engineering and the Physical Sciences
Introduction to Applied Statistics
Introduction to Statistical Inference
Data Mining and Analysis
Introduction to Statistical Learning
Introduction to Stochastic Processes I
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Applied Machine Learning
Logic Programming
Introduction to the Theory of Computation
Computational Logic
Design and Analysis of Algorithms
The Practice of Theory Research
Continuous Mathematical Methods with an Emphasis on Machine Learning
Machine Learning with Graphs
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Machine Learning Theory
Deep Learning
Mining Massive Data Sets
Computational Complexity
Introduction to Cryptography
Quantum Computing
Data for Sustainable Development
Computational Models of the Neocortex
Game Theory and Economic Applications
Behavioral Economics
Honors Game Theory
Introduction to Scientific Computing
Decision Analysis I: Foundations of Decision Analysis
Metalogic
Computability and Logic
Modal Logic
Topics in Mathematical Logic: Non-Classical Logic
Philosophy of Mathematics
Formal Epistemology
Seminar on Philosophy of Logic and Mathematics
Topics in Logic, Information and Agency
Judgment and Decision-Making
Computation and Cognition: The Probabilistic Approach
Computational Neuroimaging
Neural Network Models of Cognition
Brain and Decision
Theoretical Neuroscience
Large-Scale Neural Network Modeling for Neuroscience
Advanced Statistical Modeling
The Politics of Algorithms
Machine Learning Methods for Neural Data Analysis
Contingent Electives
If any of requirements 1-3 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Mathematical Models and Medical Decisions
Algebraic Error Correcting Codes
Computational Complexity II
Counting and Sampling
Information Theory and Statistics
Proofs and Modern Mathematics
Graph Theory
Functions of a Real Variable
Groups and Rings
Introduction to Topology and Geometry
Elementary Theory of Numbers
Fundamental Concepts of Analysis
Randomness: Computational and Philosophical Approaches
Introduction to Regression Models and Analysis of Variance
Applied Multivariate Analysis
Introduction to Stochastic Processes II
Random Processes on Graphs and Lattices
Total Units15-25

Media and Communication Concentration

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Media and Communication. All courses must be taken for 3 units of more
Introduction3-5
Introduction to Communication
Statistical and Data Analysis Methods3-5
Select one of the following:
Data Analysis for Quantitative Research
Data Challenge Lab
Machine Learning
Introduction to Applied Statistics
Fundamentals of Data Science: Prediction, Inference, Causality
Advanced Statistical Modeling
Introduction to Data Analysis
Introduction to Statistical Methods: Precalculus
Data Science 101
Statistical Methods in Engineering and the Physical Sciences
Introduction to Applied Statistics
Introduction to Statistical Inference
Data Mining and Analysis
Research Methods3-5
A course on empirical and computational methods that are commonly used for research on media and communication. One of the following:
Communication Research Methods
Web Applications
Introduction to Human-Computer Interaction Design
Human-Computer Interaction: Foundations and Frontiers
Data Visualization
Intersectional Qualitative Approaches
Introduction to Data Science
Introduction to Qualitative Research Methods
Beyond Bits and Atoms - Lab
Beyond Bits and Atoms: Designing Technological Tools
Qualitative Research Methodology
Design Experiments
Networks
Introduction to Computational Social Science
Optimization of Uncertainty and Applications in Finance
Introduction to Philosophy of Science
Data Science for Politics
Causal Inference for Social Science
Experimental Methods
Introduction to Computational Social Science
Foundations of Social Research
Computational Undergraduate Research
Social Network Methods
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
Doing STS: Introduction to Research
Effects, Ethics, and Policy3-5
A course on the effects of, and possible responses to, digital technology, media, and communication. For example, one of the following:
Funkentelechy: Technologies, Social Justice and Black Vernacular Cultures
Thinking Technology: Anthropological Perspectives
Whose Ghost in the Machine? Cultures, Politics and Morals of Artificial Intelligence
Media, Culture, and Society
Media Processes and Effects
The Rise of Digital Culture
Truth, Trust, and Tech
Perspectives on American Journalism
Deliberative Democracy and its Critics
Personality and Digital Media
Free Speech, Democracy and the Internet
The Politics of Algorithms
Campaigns, Voting, Media, and Elections
The Psychology of Communication About Politics in America
Virtual People
Media Psychology
Ethics, Public Policy, and Technological Change
Race and Media
Media, Technology, and the Body
Digital Civil Society
Advanced Studies in Behavior and Social Media
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Law, Order, & Algorithms
Networks and Human Behavior
Media Markets and Social Good
A.I.-Activism-Art
Politics of Data: Algorithmic Culture, Big Data, and Information Waste
Regulating Artificial Intelligence
Digital Technology and Law: Foundations
AI and Rule of Law: A Global Perspective
Language, Gender, & Sexuality
Networks
Future of Work: Issues in Organizational Learning and Design
Data Privacy and Ethics
Social and Ethical Issues in the Neurosciences
Universal Basic Income: the philosophy behind the proposal
Public Opinion and American Democracy
Money in Politics
Psychology of Xenophobia
Intergroup Communication
Social Networks
Gender and Technology
Introduction to Social Networks
Public Interest Tech: Case Studies
The Public Life of Science and Technology
Digital Technology, Society, and Democracy
Integrative Requirement3-5
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
The Psychology of Communication About Politics in America
Virtual People
Media Psychology
Advanced Digital Media Journalism
Big Local Journalism: a project-based class
Programming in Journalism
Building News Applications
Advanced Studies in Behavior and Social Media
Language and Technology
Advanced Topics in Human Virtual Representation
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Exploring Computational Journalism
Machine Learning with Graphs
Social Computing
The Structure of Discourse: Theory and Applications
Language and Society
The Politics of Algorithms
Digital Technology, Society, and Democracy
Contingent Electives3-5
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Semiotics for Ethnography
Advanced Statistical Methods for Observational Studies
Advanced Data Analysis in Qualitative Research
Introduction to Linguistics
Social Bias and Earwitness Memory
Linguistic Meaning and Legal Interpretation
The Structure of Discourse: Theory and Applications
Analysis of Variation
Programming for Linguists
Spoken Language Processing
Introduction to Personality and Affective Science
Introduction to Comparative Studies in Race and Ethnicity
Psychometrics and automated experiment design
Data Mining and Analysis
Introduction to Regression Models and Analysis of Variance
Total Units15-25

Natural Language

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Natural Language. All courses must be taken for 3 units of more.
Students in the Natural Language Concentration must take four courses from at least 3 of areas 1-7, plus a course from area 8. If any of requirements 1-7 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses (see area 9) must be completed to total 5 courses beyond those that are taken for the Core.
Mathematical/Computational Foundations3-5
Introduction to the Theory of Computation
Artificial Intelligence: Principles and Techniques
Machine Learning
Modal Logic
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Experimental Methods
Affective Neuroscience
Computational Linguistics3-5
From Languages to Information
Natural Language Processing with Deep Learning
Spoken Language Processing
Natural Language Understanding
Information Retrieval and Web Search
Natural Language Processing & Text-Based Machine Learning in the Social Sciences
Challenges for Language Systems
Phonetics/Phonology/Speech3-5
Phonetics
Introduction to Phonology
Seminar in Phonology: Stress, Tone, and Accent
Sociophonetics
Advanced Phonetics
Advanced Phonetics
Phonology
Corpus Phonology
Historical Morphology and Phonology
Morphosyntax3-5
The Syntax of English
Crosslinguistic Syntax
Morphosyntax
Foundations of Syntactic Theory I
Seminar in Syntax: Advanced Topics
Historical Morphosyntax
Semantics/Pragmatics/Philosophy of Language3-5
Introduction to Semantics and Pragmatics
Introduction to Lexical Semantics
Lexical Semantic Typology
Advanced Semantics
Advanced Topics in Semantics & Pragmatics
Lexical Semantics
Seminar in Semantics: Conditionals
Wittgenstein
Philosophy of Language
Advanced Philosophy of Language
Naturalizing Representation
Capstone Seminar: Artificial Intelligence
Slurs and Derogation: Semantic, Pragmatic and Ethical Perspectives
Evolution of Signalling
Advanced Topics in Philosophy of Language
Challenges for Language Systems
Psycholinguistics3-5
Learning to Speak: An Introduction to Child Language Acquisition
Methods in Psycholinguistics
Foundations of Psycholinguistics
Seminar in Developmental Psycholinguistics
Language and Thought
Introduction to Psycholinguistics
Neural Network Models of Cognition
Sociolinguistics and Language Change3-5
African American Vernacular English
Introduction to Word-Formation
Language and Society
Who Speaks Good English
Sociolinguistics and Pidgin Creole Studies
Language, Gender, & Sexuality
Sociophonetics
American Dialects
Introduction to Linguistic Typology
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Language and Technology
Artificial Intelligence: Principles and Techniques
Information Retrieval and Web Search
From Languages to Information
Wittgenstein
Philosophy of Language
Advanced Philosophy of Language
Naturalizing Representation
Capstone Seminar: Artificial Intelligence
Slurs and Derogation: Semantic, Pragmatic and Ethical Perspectives
Evolution of Signalling
Logic and Artificial Intelligence
Research Seminar on Logic and Cognition
Topics in Logic, Information and Agency
Advanced Topics in Philosophy of Language
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Topics in Natural and Artificial Intelligence
The Philosophy and Science of Perception
Conceptual Issues in Cognitive Science
Contingent Electives3-5
If any of requirements 1-7 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Introduction to Statistical Methods: Precalculus
Introduction to Applied Statistics
Introduction to Statistical Inference
Additional courses may be added here in the future.
Total Units15-25

Neurosciences

See also the Symbolic Systems website.

Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Neurosciences. All courses must be taken for 3 units of more.
Students in the Neurosciences Concentration must take a total of five courses. At least two of the five courses must be from the first two areas, and at least one must come from area 7. If any of the courses listed under areas 1-6 are taken for Core requirements, then additional approved Contingent Elective courses (see area 8) must be completed to total 5 courses beyond those that are taken for the Core. Area 9 (Recommended Add-ons) consists of one- and two-unit courses that supplement areas 1-8. Add-on courses do not count toward the 5-course requirement for the Concentration.
Basic Neuroscience3-5
Physiology
Cell Biology
Human Behavioral Biology
Mechanisms of Neuron Death
Cellular Neuroscience: Cell Signaling and Behavior
Molecular and Cellular Neurobiology
The Human Organism
The Nervous System
Information and Signaling Mechanisms in Neurons and Circuits
Ion Transport and Intracellular Messengers
Cognitive Development
Foundations of Cognition
Note: NBIO 206 is a 6-unit course, which counts as two concentration courses, from areas 1 and 2.
Systems Neuroscience3-5
Developmental Neurobiology
Exploring Neural Circuits
Educational Neuroscience
Brain Plasticity
Introduction to Perception
Introduction to Learning and Memory
Introduction to Cognitive Neuroscience
Brain Networks
Advanced Seminar on Memory
Brain and Decision
Affective Neuroscience
Current Debates in Learning and Memory
Computational Approaches3-5
Systems Biology
Quantitative Physiology
Introduction to Robotics
Machine Learning
Computational Models of the Neocortex
Introduction to Neuroelectrical Engineering
Materials Advances for Neurotechnology: Materials Meet the Mind
Neuroplasticity and Musical Gaming
Brain decoding
Computation and Cognition: The Probabilistic Approach
Human Neuroimaging Methods
Computational Neuroimaging
Neural Network Models of Cognition
Large-Scale Neural Network Modeling for Neuroscience
Brain Machine Interfaces: Science, Technology, and Application
Machine Learning Methods for Neural Data Analysis
Biological and Computational Approaches to Vision3-5
Computer Vision: Foundations and Applications
Computer Vision: From 3D Reconstruction to Recognition
Convolutional Neural Networks for Visual Recognition
Introduction to Perception
Image Systems Engineering
High-level Vision: From Neurons to Deep Neural Networks
Philosophical and Theoretical Approaches3-5
Social and Ethical Issues in the Neurosciences
Philosophy of Neuroscience
Philosophy of Mind
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Theoretical Neuroscience
Conceptual Issues in Cognitive Science
Methodological Foundations3-5
Principles and Practice of Optogenetics for Optical Control of Biological Tissues
Continuous Mathematical Methods with an Emphasis on Machine Learning
Data Visualization
Signal Processing and Linear Systems I
Signal Processing and Linear Systems II
The Fourier Transform and Its Applications
Introduction to Linear Dynamical Systems
Linear Algebra and Matrix Theory
Introduction to Optimization
Introduction to Statistical Methods: Precalculus
Research Methods in Cognition & Development
Human Neuroimaging Methods
Experimental Methods
Statistical Methods for Behavioral and Social Sciences
Advanced Statistical Modeling
Statistical Methods in Engineering and the Physical Sciences
Biostatistics
Introduction to Applied Statistics
Introduction to Statistical Inference
Integrative Requirement3-5
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Computer Vision: Foundations and Applications
Artificial Intelligence: Principles and Techniques
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Deep Learning
Computer Vision: From 3D Reconstruction to Recognition
Reinforcement Learning
Computational Models of the Neocortex
Philosophy of Neuroscience
Research Seminar on Logic and Cognition
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Topics in Neurodiversity: Design Thinking Approaches
Ion Transport and Intracellular Messengers
Brain Networks
Brain decoding
Advanced Seminar on Memory
Cognitive Neuroscience
Computation and Cognition: The Probabilistic Approach
Human Neuroimaging Methods
Computational Neuroimaging
Neural Network Models of Cognition
Brain and Decision
Theoretical Neuroscience
Topics in Natural and Artificial Intelligence
Large-Scale Neural Network Modeling for Neuroscience
Affective Neuroscience
Machine Learning Methods for Neural Data Analysis
Theories of Consciousness
The Philosophy and Science of Perception
Conceptual Issues in Cognitive Science
Cognition in Interaction Design
Contingent Electives3-5
If any of the courses listed under areas 1-6 are taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core. These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Additional courses may be added here in the future.
Recommended Add-ons3-5
One- and two-unit courses that supplement the offerings above. These courses are recommended, but do not count toward the 5-course requirement for the Concentration:
NeuroTech Training Seminar
Experimental Immersion in Neuroscience
Total Units15-25

Philosophical Foundations

See also the Symbolic Systems website.

Units
Symbolic Systems majors completing the new Core requirements effective for 2020-2021 must complete the following requirements to qualify for a Concentration in Philosophical Foundations. All courses must be taken for 3 units of more.
Philosophy of Mind and Language3-5
One course from the PHIL 180-series:
Metaphysics
Realism, Anti-Realism, Irrealism, Quasi-Realism
Philosophy of Language
Advanced Philosophy of Language
Naturalizing Representation
Truth
Self-knowledge and Metacognition
Topics in Epistemology
Formal Epistemology
Topics in the Theory of Justification
Special Topics in Epistemology: Testimony in science and everyday life
Metaontology
Philosophy of Mind
Philosophy of Action
Paradoxes
Fine-Tuning Arguments for God's Existence
Ethics, Historical, and Political Philosophy3-5
Courses must be numbered 100 or above.
Select one of the following:
Modern Philosophy, Descartes to Kant
Plato's Later Metaphysics and Epistemology
History of Modern Moral Philosophy
Metaethics
Philosophy of Law
The Meaning of Life
Logic3-5
Select one of the following:
Introduction to the Theory of Computation
Computability and Logic
Modal Logic
Topics in Logic, Information and Agency
Philosophy of Science3-5
Select one of the following:
Philosophy of Artificial Intelligence
Philosophy of Mathematics
Central Topics in the Philosophy of Science: Theory and Evidence
Philosophy of Physics: Space and Time
Philosophy of Neuroscience
Evolution of the Social Contract
Conceptual Issues in Cognitive Science
Integrative Requirement
Must be completed no earlier than the Junior Year.
i. Any of the Standard Options for all Concentrations specified under the Core Capstone requirement, or
ii. A Concentration-Specific Integrative Course: A course that integrates the themes of the Concentration with the Core requirements. Select one of the following (with more options to be added as they are approved -- some options may be removed if they are included in the list of SYMSYS 195* project courses, in order to avoid redundancy with the Standard Options).
Computers, Ethics, and Public Policy
Ethics, Public Policy, and Technological Change
Social and Ethical Issues in the Neurosciences
Phenomenology: Animals
Philosophy of Mathematics
Philosophy of Neuroscience
Evolution of the Social Contract
Formal Epistemology
Capstone Seminar: Artificial Intelligence
Capstone seminar: Common Sense Philosophy
What makes a good explanation? Psychological and philosophical perspectives
Topics in Logic, Information and Agency
Grad Seminar: Philosophy of Neuroscience
Topics in Neuroscience
Topics in Metaphysics and Epistemology: Situations and Attitudes
Seminar on Emotion
Theories of Consciousness
The Philosophy and Science of Perception
Conceptual Issues in Cognitive Science
Contingent Electives
If any of requirements 1-4 are fulfilled with courses taken for Core requirements, then additional approved Contingent Elective courses must be completed to total 5 courses beyond those that are taken for the Core: These electives can be one or more courses from any of the areas above, or which are approved for a Core requirement that the student has fulfilled with a different course, or any of the following:
Additional courses may be added here in the future.
Total Units15-25

Individually Designed Concentrations (IDCs)

Individually Designed Concentrations (IDCs) consist of five courses in a coherent subject area related to symbolic systems. This relationship may be established through inclusion in an IDC of two or more courses that connect the proposed concentration to the core, i.e. courses that (a) directly apply disciplines included in the core and (b) are related by topic or methodology to the other courses in the proposed concentration.

Course selection is to be made in consultation with the student's adviser and is subject to approval by the adviser, the Associate Director, and the Director. For examples of IDCs completed by past SSP students, consult the list of alumni and apply the filter "Individually Designed Concentration".

Approval of an IDC must take place no less than two full quarters before a student plans to graduate, e.g. prior to the first day of Winter Quarter of the senior year if a student intends to graduate in June of that year. Failure to obtain approval by the required date will necessitate either completing the requirements for one of the suggested concentrations, or delaying graduation to the end of the second full quarter following approval of an IDC.

To get a proposed IDC approved, send an email message to symsys-directors at lists.stanford.edu, cc'd to your prospective concentration adviser, stating that the adviser has approved your proposal, and giving a title, one-paragraph description, and course plan for your proposed concentration.

Additional Information

Undergraduate Research

The program encourages all SSP majors to gain experience in directed research by participating in faculty research projects or by pursuing independent study. In addition to the Symbolic Systems Honors Program (see below), the following avenues are offered.

Summer Internships: students work on SSP-related faculty research projects. Application procedures are announced in the Winter Quarter for SSP majors. 

Research Assistantships: other opportunities to work on faculty research projects are typically announced to SSP majors as they arise during the academic year. 

Independent Study: under faculty supervision. For course credit, students should enroll in SYMSYS 196 Independent Study.

Contact SSP for more information on any of these possibilities, or see the Symbolic Systems web site. In addition, see the Undergraduate Advising and Research web site for information on UAR grants and scholarships supporting student research projects at all levels.

Honors Program

Seniors in SSP may apply for admission to the Symbolic Systems honors program prior to the beginning of their final year of study. Students who are accepted into the honors program can graduate with honors by completing an honors thesis under the supervision of a faculty member. Course credit for the honors project may be obtained by registering for SYMSYS 190 Senior Honors Tutorial any quarter while a student is working on an honors project. SYMSYS 191 Senior Honors Seminar, is recommended for honors students during the senior year. Contact SSP or visit the program's web site for more information on the honors program, including deadlines and policies.

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Minor in Symbolic Systems

Students may minor in Symbolic Systems by completing either Option 1 or Option 2. For additional information see the Symbolic Systems minors web site.

Degree Requirements

Option 1

Units
One course in each of the following core areas (please note that several of these courses have prerequisites):
a. Cognition3-4
Select one of the following:
Minds and Machines (formerly SYMSYS 100)
Introduction to Learning and Memory
Introduction to Cognitive Neuroscience
b. Logic and Computation3-5
Select one of the following:
Mathematical Logic
Metalogic
Mathematical Foundations of Computing
c. Computer Programming3-5
Select one of the following:
Programming Abstractions
Programming Abstractions
Computer Organization and Systems
d. Philosophical Foundations4-5
Select one of the following:
Minds and Machines (formerly SYMSYS 100)
Mind, Matter, and Meaning
e. Linguistic Theory3-4
Select one of the following:
Phonetics
Introduction to Phonology
Introduction to Syntax
The Syntax of English
Crosslinguistic Syntax
Introduction to Semantics and Pragmatics
Introduction to Lexical Semantics
f. Computation and Cognition3-4
Select one of the following:
Theoretical Neuroscience
Artificial Intelligence: Principles and Techniques
Probabilistic Graphical Models: Principles and Techniques
Machine Learning
Computer Vision: Foundations and Applications
From Languages to Information
LINGUIST 182
(no longer offered)
Computational Neuroscience
An introduction to computation and cognition
Computation and Cognition: The Probabilistic Approach
Neural Network Models of Cognition
Total Units19

Option 2

Introduction4
SYMSYS 1Minds and Machines (formerly SYMSYS 100)4
Interdisciplinary Concentration15
An interdisciplinary SSP concentration listed on the SSP web site. To qualify, the selection of courses used for the minor must be interdisciplinary; it must either include courses from at least three departments, or include more than one course from each of two departments.
Total Units19

Coterminal Master's Degrees in Symbolic Systems

The Symbolic Systems M.S. Program admits a handful of coterminal students each year. Coterminal students usually complete the program in one academic year.

Applications for Coterminal admission of active Stanford undergraduates are reviewed in the Winter and Spring Quarters. For more details, see the Coterm admissions information on the Symbolic Systems Program website. Admission to the program as a coterminal student is subject to the policies and deadlines described in the "Coterminal Bachelor's and Master's Degrees" section of this bulletin. The GRE is not required for coterminal applicants to the Symbolic Systems M.S. program.

Many SSP majors also complete coterminal M.S. or M.A. degrees in affiliated departments. In addition to the Symbolic Systems M.S. program, the Department of Philosophy offers a Special Program in Symbolic Systems track for interdisciplinary graduate level work leading to the Master of Arts in Philosophy.

University Coterminal Requirements

Coterminal master’s degree candidates are expected to complete all master’s degree requirements as described in this bulletin. University requirements for the coterminal master’s degree are described in the “Coterminal Master’s Program” section. University requirements for the master’s degree are described in the "Graduate Degrees" section of this bulletin.

After accepting admission to this coterminal master’s degree program, students may request transfer of courses from the undergraduate to the graduate career to satisfy requirements for the master’s degree. Transfer of courses to the graduate career requires review and approval of both the undergraduate and graduate programs on a case by case basis.

In this master’s program, courses taken during or after the first quarter of the sophomore year are eligible for consideration for transfer to the graduate career; the timing of the first graduate quarter is not a factor. No courses taken prior to the first quarter of the sophomore year may be used to meet master’s degree requirements.

Course transfers are not possible after the bachelor’s degree has been conferred.

The University requires that the graduate advisor be assigned in the student’s first graduate quarter even though the undergraduate career may still be open. The University also requires that the Master’s Degree Program Proposal be completed by the student and approved by the department by the end of the student’s first graduate quarter.

Master of Science in Symbolic Systems

The University's basic requirements for the M.S. degree are discussed in the "Graduate Degrees" section of this bulletin.

The M.S. degree in Symbolic Systems is designed to be completed in the equivalent of one academic year by coterminal students or returning students who already have a B.S. degree in Symbolic Systems, and in two years or less by other students depending upon level of preparation. Admission is competitive, providing a limited number of students with the opportunity to pursue course and project work in consultation with a faculty adviser who is affiliated with the Symbolic Systems Program. The faculty adviser may impose requirements beyond those described here.

Admission to the program as a coterminal student is subject to the policies and deadlines described in the "Coterminal Bachelor's and Master's Degrees" section of this bulletin. Applicants to the M.S. program are reviewed each Winter Quarter. Information on deadlines, procedures for applying, and degree requirements are available from the program's student services coordinator in the Linguistics Department office (460-127E) and at the Symbolic Systems web site.

Note, the GRE is required for external applicants. 

Symbolic Systems also offers a Joint Degree with Law School (M.S./J.D.).

Director of Graduate Studies: Hyowon Gweon 

Degree Requirements

A candidate for the M.S. degree in Symbolic Systems must complete a program of 45 units. All courses must be 100-level and above. At least 36 of these must be graded units, passed with an average grade of 3.0 (B) or better, and any course taken as part of the 45 unit program must be taken for a letter grade unless the course is offered S/NC only. None of the 45 units to be counted toward the M.S. degree may include units counted toward an undergraduate degree at Stanford or elsewhere. Course requirements for the M.S. degree in Symbolic Systems may be waived after a review by the program office. Waivers are granted at the discretion of the program, and only if evidence is provided that similar or more advanced courses have been taken and passed with a letter grade of B or its equivalent, either at Stanford or another institution, and as part of another degree program which the student has either completed or is pursuing in parallel with the Symbolic Systems M.S. degree. Course requirements that are waived rather than fulfilled by courses taken at Stanford may not be counted toward the 45 units required for the Symbolic Systems M.S. degree. For additional information, see the Symbolic Systems web site.

Each candidate for the M.S. degree must fulfill the following requirements:

  1. Submission to the Symbolic Systems Program office and approval of the following pre-project research documents:
    1. Project Area Statement, endorsed with a commitment from a student's prospective project adviser no later than May 1 of the academic year prior to the expected graduation year; and
    2. Qualifying Research Paper due no later than the end of the Summer Quarter prior to the expected graduation year.
  2. Completion of a coherent plan of study, to be approved by the Program Director, Director of Graduate Studies, or Associate Director, in consultation with the student's primary adviser (for students with an approved Project Area Statement), and designed to support a student's project as well as the core course requirements for the M.S. degree (requirements 3 and 4 below). An initial plan of study should be delineated on the Program Proposal Form prior to the end of the student's first quarter of study, as required by the University. The final version of the Program Proposal, which should specify all the courses which the student has taken and proposes in fulfillment of both the Program's and the University's course and unit requirements for the degree, is due by the end of Finals Week in the quarter prior to the student's expected graduation quarter (i.e. end of Winter Quarter for a student graduating in the Spring).
  3. Completion of the Master's Breadth Requirements. The Program Proposal must include courses taken for 3 units or more each that are more advanced than the Symbolic Systems undergraduate core in four main skill areas: formal, empirical, computational, and philosophical; and in at least three of the following departments (based on the listing as as any cross-listing departments): Computer Science, Linguistics, Philosophy, and Psychology. Courses to fulfill the Breadth Requirements must be taken for a letter grade if available.

Acceptable courses in each of the four required skill areas are defined as follows:

a) Formal: a course in logic and computational theory beyond the level of PHIL 151 Metalogic. The courses below have been approved. Other courses may be approved if appropriate.

b) Empirical: a course drawing on experimental or observational data or methods, beyond the level of PSYCH 55, LINGUIST 120 or 130A. The courses below are examples of those that have been approved. Other courses may be approved if appropriate.

  • CS 224N Natural Language Processing with Deep Learning
  • CS 224U Natural Language Understanding
  • CS 229 Machine Learning
  • CS 376 Research Topics in Human-Computer Interaction
  • LINGUIST 230B Advanced Semantics
  • NBIO 206 The Nervous System
  • NBIO 258 Information and Signaling Mechanisms in Neurons and Circuits
  • PSYCH 204 Computation and Cognition: The Probabilistic Approach
  • PSYCH 204A Human Neuroimaging Methods
  • PSYCH 209 Neural Network Models of Cognition
  • PSYCH 251 Experimental Methods
  • PSYCH 252 Statistical Methods for Behavioral and Social Sciences
  • STATS 200 Introduction to Statistical Inference
  • SYMSYS 245 Cognition in Interaction Design

c) Computational: a course involving programming beyond the level of CS 107. The courses below have been approved. Other courses may be approved if appropriate.

  • CS 108 Object-Oriented Systems Design
  • CS 110 Principles of Computer Systems
  • CS 124 From Languages to Information
  • CS 142 Web Applications
  • CS 143 Compilers
  • CS 145 Data Management and Data Systems
  • CS 148 Introduction to Computer Graphics and Imaging
  • CS 210A Software Project Experience with Corporate Partners
  • CS 221 Artificial Intelligence: Principles and Techniques
  • CS 224N Natural Language Processing with Deep Learning
  • CS 224W Machine Learning with Graphs
  • CS 246 Mining Massive Data Sets

d) Philosophical: a course in the area of Philosophy of Mind/Language/Science/Epistemology or Metaphysics at the 200 level or above, certified by the instructor as worthy of graduate credit. The courses below are examples of those that have been approved. Other courses may be approved if appropriate.

4. Completion of three quarters of SYMSYS 291 Master's Program Seminar.

5. Completion of a substantial project appropriate to the Program Proposal, represented by the M.S. Thesis. The project and thesis normally take three quarters or more to complete, and work on the project may account for up to 15 units of a student's 45-unit program. The thesis must be read and approved for the master's degree in Symbolic Systems by two qualified readers approved by the program, at least one of whom must be a member of the academic council. A hard copy of the thesis must be submitted to the Associate Director of Symbolic Systems, including the signatures of each reader indicating approval of the thesis for the degree of Master of Science, no later than 12 noon on the day of the University Dissertation/Thesis Submission Deadline for the quarter of a student's graduation. A digital copy must be uploaded to the Stanford Digital Repository by the same deadline. For more details, see the Master's Thesis information on the Symbolic Systems Program website.

COVID-19 Policies

On July 30, the Academic Senate adopted grading policies effective for all undergraduate and graduate programs, excepting the professional Graduate School of Business, School of Law, and the School of Medicine M.D. Program. For a complete list of those and other academic policies relating to the pandemic, see the "COVID-19 and Academic Continuity" section of this bulletin.

The Senate decided that all undergraduate and graduate courses offered for a letter grade must also offer students the option of taking the course for a “credit” or “no credit” grade and recommended that deans, departments, and programs consider adopting local policies to count courses taken for a “credit” or “satisfactory” grade toward the fulfillment of degree-program requirements and/or alter program requirements as appropriate.


Undergraduate Degree Requirements

Grading

The Symbolic Systems Program counts all courses taken in academic year 2020-21 with a grade of 'CR' (credit) or 'S' (satisfactory) towards satisfaction of undergraduate degree requirements that otherwise require a letter grade. The program also continues to count courses passed with a' C'- letter grade or above towards the satisfaction of all core requirements, and with a 'D-' or above towards the satisfaction of concentration requirements.

Other Policies

The deadline for juniors to declare a concentration advisor has been extended to Winter Quarter. A registration hold will be placed on juniors who have not declared a concentration advisor before registration opens for Spring Quarter 2020-21.
 

Graduate Degree Requirements

Grading

The master's program in Symbolic Systems counts all courses taken in academic year 2020-21 with a grade of 'D-', 'CR' (credit) or 'S' (satisfactory) towards satisfaction of graduate degree requirements that otherwise require a letter grade, subject to a graduate GPA requirement of 3.0 or above in the courses that constitute a master's student's 45 required units.
 

Graduate Advising Expectations

The Symbolic Systems Program is committed to providing academic advising in support of graduate student scholarly and professional development. When most effective, this advising relationship entails collaborative and sustained engagement by both the adviser and the advisee. As a best practice, advising expectations should be periodically discussed and reviewed to ensure mutual understanding. Both the adviser and the advisee are expected to maintain professionalism and integrity.

Faculty advisers guide students in key areas such as selecting courses, designing and conducting research, developing of teaching pedagogy, navigating policies and degree requirements, and exploring academic opportunities and professional pathways.

Graduate students are active contributors to the advising relationship, proactively seeking academic and professional guidance and taking responsibility for informing themselves of policies and degree requirements for their graduate program. Students are expected to meet regularly with their advisers and to keep them informed about their academic progress. Each student and their adviser should mutually agree on the frequency of these meetings when the advising relation begins and reassess their frequency at the start of every quarter.

For a statement of University policy on graduate advising, see the "Graduate Advising" section of this bulletin.

Faculty

Director: Michael C. Frank

Director of Graduate Studies: Hyowon Gweon 

Associate Director: Todd Davies

Faculty Advisory Board: Jeremy Bailenson, Michael Bernstein, Ray Briggs, Todd Davies, Judith Degen, Michael C. Frank, Noah Goodman, Hyowon Gweon, Thomas Icard, Daniel Jurafsky, Daniel Lassiter, Krista Lawlor, Christopher Manning, James McClelland, Stanley Peters, Christopher Potts, Mehran Sahami, Johan van Benthem, Thomas A. Wasow 

Executive Committee: Michael Bernstein, Todd Davies, Michael C. Frank, Hyowon Gweon, Thomas Icard, Christopher Potts

Program Faculty:

Aeronautics and Astronautics: Mykel Kochenderfer (Assistant Professor)

Biology: Deborah Gordon (Professor)

Classics: Reviel Netz (Professor)

Communication: Jeremy Bailenson (Professor), Jeff Hancock (Professor), Byron Reeves (Professor), Frederick Turner (Professor)

Computer Science:  Maneesh Agrawala (Professor), Michael Bernstein (Assistant Professor), Emma Brunskill (Assistant Professor), David Dill (Professor, emeritus), Chelsea Finn (Assistant Professor), Michael Genesereth (Associate Professor), Oussama Khatib (Professor), Daphne Koller (Adjunct Professor), James Landay (Professor), Jean-Claude Latombe (Professor, emeritus), Marc Levoy (Professor, emeritus), Christopher Manning (Professor), Andrew Ng (Adjunct Professor), Chris Piech (Assistant Professor), Vaughan Pratt (Professor, emeritus), Eric Roberts (Professor, emeritus), Mehran Sahami (Professor, Teaching), Yoav Shoham (Professor, emeritus), Terry Winograd (Professor, emeritus)

Economics: Muriel Niederle (Professor)

Education:  Nick Haber (Assistant Professor), Raymond P. McDermott (Professor, emeritus), Roy Pea (Professor), Daniel Schwartz (Professor)

Electrical Engineering: Chelsea Finn (Assistant Professor), Krishna Shenoy (Professor), Sebastian Thrun (Adjunct Professor)

French and Italian: Jean-Pierre Dupuy (Professor)

Genetics: Russ B. Altman (Professor)

Graduate School of Business: Baba Shiv (Professor)

History: Jessica G. Riskin (Professor)

Law: Mark Lemley (Professor)

Linguistics: Arto Anttila (Associate Professor), Joan Bresnan (Professor, emerita), Eve Clark (Professor, emerita), Cleo Condoravdi (Professor Research),  Judith Degen (Assistant Professor), Penelope Eckert (Professor), Vera Gribanova (Associate Professor), Boris Harizanov (Assistant Professor), Daniel Jurafsky (Professor), Ronald Kaplan (Adjunct Professor), Lauri Karttunen (Adjunct Professor), Martin Kay (Professor), Paul Kiparsky (Professor), Daniel Lassiter (Assistant Professor), Beth Levin (Professor), Christopher Manning (Professor), Stanley Peters (Professor, emeritus), Christopher Potts (Professor), Meghan Sumner (Associate Professor), Thomas A. Wasow (Professor, emeritus), Annie Zaenen (Adjunct Professor)

Management Science and Engineering: Sharad Goel (Assistant Professor), Pamela Hinds (Professor), John Ugander (Assistant Professor)

Mathematics: Persi Diaconis (Professor)

Mechanical Engineering: Sean Follmer (Assistant Professor)

Medicine: Russ B. Altman (Professor), Mark Musen (Professor)

Music: Jonathan Berger (Professor), Christopher Chafe (Professor), Eleanor Selfridge-Field (Adjunct Professor), Ge Wang (Associate Professor)

Neurobiology: William T. Newsome (Professor), Jennifer Raymond (Professor)

Philosophy:  Michael Bratman (Professor), Ray Briggs (Professor), Rosa Cao (Assistant Professor), Mark Crimmins (Associate Professor), John Etchemendy (Professor), Dagfinn Føllesdal (Professor, emeritus), Thomas Icard III (Assistant Professor), Krista Lawlor (Professor), Anna-Sara Malmgren (Assistant Professor), John Perry (Professor, emeritus), Brian Skyrms (Professor), Johan van Benthem (Professor), Thomas A. Wasow (Professor, emeritus)

Psychiatry and Behavioral Sciences: Vinod Menon (Professor)

Psychology:  Herbert H. Clark (Professor, emeritus), Anne Fernald (Associate Professor), Michael C. Frank (Associate Professor), Justin Gardner (Assistant Professor), Noah Goodman (Associate Professor), Kalanit Grill-Spector (Professor), Hyowon Gweon (Assistant Professor), Brian Knutson (Professor), Ellen Markman (Professor), James McClelland (Professor), Russell Poldrack (Professor), Barbara Tversky (Professor, emerita), Anthony Wagner (Professor), Brian Wandell (Professor), Daniel Yamins (Assistant Professor), Jamil Zaki (Assistant Professor)

Statistics: Persi Diaconis (Professor), Susan P. Holmes (Professor)

Symbolic Systems: Todd Davies (Associate Director), Jeff Shrager (Adjunct Professor), Paul Skokowski (Adjunct Professor)

Other Affiliates: David Barker-Plummer (CSLI Engineering Research Associate), Keith Devlin H-STAR Operation Senior Researcher), Daniel Flickinger (CSLI Research and Development Engineer), Cheryl Phillips (Lecturer in Communications)

Courses

SYMSYS 1. Minds and Machines. 4 Units.

(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites.
Same as: CS 24, LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 200

SYMSYS 1P. A Practical Introduction to Symbolic Systems. 2 Units.

An optional supplement to "Minds and Machines" (SYMSYS 1), aimed at prospective majors in Symbolic Systems. Students will learn from the perspectives of faculty, alums, and advanced students about how to navigate the many paths available to a student: Sym Sys versus other majors, undergraduate core options, selecting courses and a concentration, research opportunities, internships, the honors program, graduate programs, careers, and life paths.

SYMSYS 2S. Introduction to Cognitive Science. 3 Units.

Cognitive Science explores one of sciences final frontiers; the scientific study of the human mind. It is a broad interdisciplinary field that encompasses research from areas in neuroscience, psychology, philosophy, linguistics, and computer science and covers topics such as the nature of knowledge, thinking, remembering, vision, imagery, language, and consciousness. All of which we will touch upon in this survey course and is intended to give students a sampler of each discipline. This introductory class will expose students to some of the major methodologies, experimental design, neuroscientific fundamentals, and different cognitive disorders. More importantly, it will help students refine their interest to a specific field within cognitive science for future studies at their respective institutions. This 6-week summer course will require a sizable amount of required reading, not all of the readings is covered in the lectures. To extend and complement topics in this field, there is material presented in the lectures that is not in the readings.

SYMSYS 8. The Logic Group. 1-2 Unit.

If all dogs bark and Fido is a dog, it follows that Fido barks. If Clark Kent owns a car, it follows that Superman owns a car, since Clark Kent is Superman. Yet you might wonder why these statements follow from the said assumptions. Can this perhaps be explained in terms of the statements¿ meanings or their grammatical form? Will the explanation be the same in both cases, or do statements follow from assumptions for a variety of different reasons? Are there laws or principles which conclusively prove the statements from the assumptions? Can these laws be doubted, or are they self-evident?nThe Logic Group will tackle these and similar questions. You will gain a solid understanding of both propositional and predicate logic, including a deductive proof system. You will familiarise yourself with the central concepts of formal reasoning, including syntax and semantics, truth and interpretation, validity and soundness, and the concept of logical consequence. Although formal and technical, the course is accessible to all students, and all may benefit. Studying logic will improve your analytic and critical thinking skills and help you develop a more rigorous and precise writing style. Only open to students residing at Stanford House in Oxford (UK).
Same as: Oxford

SYMSYS 20Q. The Data-Driven World. 3 Units.

Recent technological advancements have enabled us to measure, record, and analyze more data than ever before. How can we effectively use this data to solve real-world problems and better understand the world around us? In this course, we will learn how computers can create a statistical model to learn from human-generated data and find patterns or make predictions. We will explore different algorithms that create a wide variety of models, each with their own pros and cons. Through R programming exercises integrated across the course, we will apply these models to many different kinds of data sourced from urban development, education, business, etc. and analyze our findings. Based on individual interest, students will choose to investigate a specific research question using domain-specific data as part of a quarter-long project. Lastly, we will discuss important ethical debates on the possible uses of data and their implications in today¿s world. By the end of the course, students will develop a technical coding skillset to investigate hypotheses in any given dataset, and be able to connect the insights they derive to larger issues of society, equity, and justice.

SYMSYS 112. Challenges for Language Systems. 3-4 Units.

Parallel exploration of philosophical and computational approaches to modeling the construction of linguistic meaning. In philosophy of language: lexical sense extension, figurative speech, the semantics/pragmatics interface, contextualism debates. In CS: natural language understanding, from formal compositional models of knowledge representation to statistical and deep learning approaches. We will develop an appreciation of the complexities of language understanding and communication; this will inform discussion of the broader prospects for Artificial Intelligence. Special attention will be paid to epistemological questions on the nature of linguistic explanation, and the relationship between theory and practice. PREREQUISITES: PHIL80; some exposure to philosophy of language and/or computational language processing is recommended.
Same as: SYMSYS 212

SYMSYS 115. Critique of Technology. 3-4 Units.

What is the character of technology? How does technology reveal aspects of human nature and social practices? How does it shape human experience and values? We will survey the history of philosophy of technology -- from ancient and enlightenment ideas, to positivist and phenomenological conceptions -- to develop a deeper understanding of diverse technological worldviews. This will prepare us to consider contemporary questions about the "ethos" of technology. Specific questions will vary depending upon the interests of participants, but may include: ethical and existential challenges posed by artificial intelligence; responsible product design in the "attention economy"; industry regulation and policy issues for information privacy; and the like. PREREQUISITES: PHIL80.

SYMSYS 122. Artificial Intelligence: Philosophy, Ethics, & Impact. 3-4 Units.

Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of this course is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.

SYMSYS 167D. Philosophy of Neuroscience. 4 Units.

How can we explain the mind? With approaches ranging from computational models to cellular-level characterizations of neural responses to the characterization of behavior, neuroscience aims to explain how we see, think, decide, and even feel. While these approaches have been highly successful in answering some kinds of questions, they have resulted in surprisingly little progress in others. We'll look at the relationships between the neuroscientific enterprise, philosophical investigations of the nature of the mind, and our everyday experiences as creatures with minds. Prerequisite: PHIL 80.n(Not open to freshmen.).
Same as: PHIL 167D, PHIL 267D

SYMSYS 190. Senior Honors Tutorial. 1-5 Unit.

Under the supervision of their faculty honors adviser, students work on their senior honors project. May be repeated for credit.

SYMSYS 191. Senior Honors Seminar. 1 Unit.

Recommended for seniors doing an honors project. Under the leadership of the Symbolic Systems program coordinator, students discuss, and present their honors project.

SYMSYS 192. Symbolic Systems in Practice. 3 Units.

A professionalization course that fulfills the Practicum requirement of the Symbolic Systems undergraduate major Capstone. Online lectures, readings, assigned exercises, and live discussions relate the Sym Sys curriculum to a substantial work experience. Must be accompanied by an approved internship totaling 64 hours or more of total work time, which must be completed in the quarter prior to, during, or immediately following the course.

SYMSYS 195A. Design for Artificial Intelligence. 3-4 Units.

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: CS147 or equivalent background in design thinking.
Same as: CS 247A

SYMSYS 195B. Design for Behavior Change. 3-4 Units.

Over the last decade, tech companies have invested in shaping user behavior, sometimes for altruistic reasons like helping people change bad habits into good ones, and sometimes for financial reasons such as increasing engagement. In this project-based hands-on course, students explore the design of systems, information and interface for human use. We will model the flow of interactions, data and context, and crafting a design that is useful, appropriate and robust. Students will design and prototype utility apps or games as a response to the challenges presented. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Prerequisite: CS147 or equivalent.
Same as: CS 247B

SYMSYS 195D. Research in Digital Democracy. 3-4 Units.

Digital democracy refers to social activity that is organized democratically at a group, institutional, or societal level, and that takes place within or is augmented by digital technology. This is a project-based research seminar designed to teach students methods for studying digital democracy, as well as collaborating in a group, the organization of a research project, and academic writing. The first few weeks of the course will be an overview of digital democracy research and its methods, as well as a time for students to organize into a group research project, The remainder of the class (about 7 weeks) will be spent performing and writing up the research for a targeted publication venue. Application required for enrollment. Prerequisite: At least one course in empirical methods or statistics. Prerequisites: At least one course in empirical methods or statistics.
Same as: SYMSYS 295D

SYMSYS 195E. Experimental Methods. 3 Units.

Graduate laboratory class in experimental methods for psychology, with a focus on open science methods and best practices in behavioral research. Topics include experimental design, data collection, data management, data analysis, and the ethical conduct of research. The final project of the course is a replication experiment in which students collect new data following the procedures of a published paper. The course is designed for incoming graduate students in psychology, but is open to qualified students from other programs who have some working knowledge of the R statistical programming language. Requirement: PSYCH 10/STATS 60 or equivalent.
Same as: PSYCH 251

SYMSYS 195G. Introduction to Game Design. 3-4 Units.

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; please plan on attending every studio to take this class. nThe focus of CS247g is an introduction to theory and practice of the design of games. We will make digital and paper games, do rapid iteration and run user research studies appropriate to game design. This class has multiple short projects, allowing us to cover a variety of genres, from narrative to pure strategy. Prerequisites: 147 or equivalent background.
Same as: CS 247G

SYMSYS 195I. Image Systems Engineering. 1-3 Unit.

This course is an introduction to digital imaging technologies. We focus on the principles of key elements of digital systems components; we show how to use simulation to predict how these components will work together in a complete image system simulation. The early lectures introduce the software environment and describe options for the course project. The following topics are covered and software tools are introduced:n- Basic principles of optics (Snell's Law, diffraction, adaptive optics).n- Image sensor and pixel designsn- Color science, metrics, and calibrationn- Human spatial resolutionn- Image processing principlesn- Display technologiesnA special theme of this course is that it explains how imaging technologies accommodate the requirements of the human visual system. The course also explains how image systems simulations can be useful in neuroscience and industrial vision applications.nThe course consists of lectures, software tutorials, and a course project. Tutorials and projects include extensive software simulations of the imaging pipeline. Some background in mathematics (linear algebra) and programming (Matlab) is valuable.nPre-requisite: EE 261 or equivalent. Or permission of instructor required.
Same as: PSYCH 221

SYMSYS 195L. Methods in Psycholinguistics. 4 Units.

Over the past ten years, linguists have become increasingly interested in testing theories with a wider range of empirical data than the traditionally accepted introspective judgments of hand-selected linguistic examples. Consequently, linguistics has seen a surge of interest in psycholinguistic methods across all subfields. This course will provide an overview of various standard psycholinguistic techniques and measures, including offline judgments (e.g., binary categorization tasks like truth-value judgments, Likert scale ratings, continuous slider ratings), response times, reading times, eye-tracking, ERPs, and corpus methods. Students will present and discuss research articles. Students will also run an experiment (either a replication or an original design, if conducive to the student¿s research) to gain hands-on experience with experimental design and implementation in html/javascript and Mechanical Turk; data management, analysis, and visualization in R; and open science tools like git/github.
Same as: LINGUIST 245B

SYMSYS 195N. Natural Language Processing with Deep Learning. 3-4 Units.

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124, CS221, or CS229.
Same as: CS 224N, LINGUIST 284

SYMSYS 195S. Service Design. 3-4 Units.

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247S is Service Design. In this course we will be looking at experiences that address the needs of multiple types of stakeholders at different touchpoints - digital, physical, and everything in between. If you have ever taken an Uber, participated in the Draw, engaged with your bank, or ordered a coffee through the Starbucks app, you have experienced a service that must have a coordinated experience for the customer, the service provider, and any other stakeholders involved. Let us explore what specialized tools and processes are required to created these multi-faceted interactions. Prerequisites: CS147 or equivalent background in design thinking.
Same as: CS 247S

SYMSYS 195T. Natural Language Processing & Text-Based Machine Learning in the Social Sciences. 4 Units.

Digital communications (including social media) are the largest data sets of our time, and most of it is text. Social scientists need to be able to digest small and big data sets alike, process it and extract psychological insight. This applied and project-focused course introduces students to a Python codebase developed to facilitate text analysis in the social sciences (see dlatk.wwbp.org -- knowledge of Python is helpful but not required). The goal is to practice these methods in guided tutorials and project-based work so that the students can apply them to their own research contexts and be prepared to write up the results for publication. The course will provide best practices, as well as access to and familiarity with a Linux-based server environment to process text, including the extraction of words and phrases, topics and psychological dictionaries. We will also practice the use of machine learning based on text data for psychological assessment, and the further statistical analysis of language variables in R. Familiarity with Python is helpful but not required. Basic familiarity with R is expected. The ability to wrangle data into a spreadsheet-like format is expected. A basic introduction to SQL will be given in the course. Familiarity with SSH and basic Linux is helpful but not required. Understanding of regression is expected.
Same as: PSYCH 290, SOC 281

SYMSYS 195U. Natural Language Understanding. 3-4 Units.

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Prerequisites: one of LINGUIST 180/280, CS 124, CS 224N, or CS 224S.
Same as: CS 224U, LINGUIST 188, LINGUIST 288

SYMSYS 195V. Data Visualization. 3-4 Units.

Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. Prerequisite: one of CS147, CS148, or equivalent.
Same as: CS 448B

SYMSYS 196. Independent Study. 1-15 Unit.

Independent work under the supervision of a faculty member. Can be repeated for credit.

SYMSYS 200. Minds and Machines. 4 Units.

(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites.
Same as: CS 24, LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1

SYMSYS 201. Digital Technology, Society, and Democracy. 3 Units.

The impact of information and communication technologies on social and political life. Interdisciplinary. Classic and contemporary readings focusing on topics such as social networks, virtual versus face-to-face communication, the public sphere, voting technology, and collaborative production. Prerequisite: Completion of a course in psychology, communication, human-computer interaction, or a related discipline, or consent of the instructor.

SYMSYS 202. Theories of Consciousness. 3 Units.

Are fish conscious? Are fetuses? Could we build a conscious computer? Much of the philosophical work on consciousness has focused on whether consciousness is wholly physical, but that question is orthogonal to the more specific questions about consciousness that most of us really care about. To answer those questions, we need a theory of how consciousness works in our world. Philosophers and scientists have put forward a spectrum of different candidates, from very abstract, philosophical theories through theories more informed by cognitive psychology down to neural and even quantum theories. In this seminar, students will learn about the major theories of consciousness as well as conceptual issues that arise on different approaches. Particularly important will be the question of how we might gain empirical evidence for a theory of consciousness.

SYMSYS 203. Cognitive Science Perspectives on Humanity and Well-Being. 3 Units.

In recent years, cognitive scientists have turned more attention to questions that have traditionally been investigated bynhistorians, political scientists, sociologists, and anthropologists, e.g. What are the sources of conflict and disagreement betweennpeople?, What drives or reduces violence and injustice?, and What brings about or is conducive to peace and justice? In this advancednsmall seminar, we will read and discuss works by psychologists, neuroscientists, philosophers, and others, which characterize thisngrowing research area among those who study minds, brains, and behavior.nRequired: Completion of a course in psychology beyond the level of PSYCH 1, or consent of the instructor.

SYMSYS 205. The Philosophy and Science of Perception. 3 Units.

Our senses tell us about our immediate environment, but what exactly do they tell us? Our color experiences tell us that the things around us have color properties, but what in the world are color properties? Do we visually represent absolute size as well as relative size? When we see an apple, do we literally see it as an apple, or do we infer that it¿s an apple based on its color and shape? Can what we expect to see affect what we actually see? In this seminar we will bring both philosophical and empirical perspectives to bear on these and other issues related to figuring out just how our perceptual experiences represent the world as being. Prerequisite: PHIL 80 or permission of the instructor.

SYMSYS 207. Conceptual Issues in Cognitive Science. 3 Units.

This seminar will cover a selection of foundational issues in cognitive science. Topics may include modularity, representation, connectionism, neuroscience and free will, neuroimaging, implants, sensory experience, the nature of information, and consciousness. Course is limited to 15 students. Prerequisite: PHIL 80, or permission of the instructor.

SYMSYS 208. Computer Machines and Intelligence. 3 Units.

It has become common for us to see in the media news about computer winning a masters in chess, or answering questions on the Jeopardy TV show, or the impact of AI on health, transportation, education, in the labor market and even as an existential threat to mankind. This interest in AI gives rise questions such as: Is it possible for a computer to think? What is thought? Are we computers? Could machines feel emotions or be conscious? Curiously, there is no single, universally accepted definition of Artificial Intelligence. However in view of the rapid dissemination of AI these questions are important not only for experts, but also for all other members of society. This course is intended for students from different majors Interested in learn how the concept of intelligent machine is understood by the researchers in AI. We will study the evolution of AI research, its different approaches, with focus on the tests developed to verify if a machine is intelligent or not. In addition, we will examine the philosophical problems associated with the concept of intelligent machine. The topics covered will include: Turing test, symbolic AI, connectionist AI, sub- symbolic Ai, Strong AI and Weak AI, Ai singularity, unconventional computing, rationality, intentionality, representation, machine learning, and the possibility of conscious machines.

SYMSYS 212. Challenges for Language Systems. 3-4 Units.

Parallel exploration of philosophical and computational approaches to modeling the construction of linguistic meaning. In philosophy of language: lexical sense extension, figurative speech, the semantics/pragmatics interface, contextualism debates. In CS: natural language understanding, from formal compositional models of knowledge representation to statistical and deep learning approaches. We will develop an appreciation of the complexities of language understanding and communication; this will inform discussion of the broader prospects for Artificial Intelligence. Special attention will be paid to epistemological questions on the nature of linguistic explanation, and the relationship between theory and practice. PREREQUISITES: PHIL80; some exposure to philosophy of language and/or computational language processing is recommended.
Same as: SYMSYS 112

SYMSYS 245. Cognition in Interaction Design. 3 Units.

Note: Same course as 145 which is no longer active. Interactive systems from the standpoint of human cognition. Topics include skill acquisition, complex learning, reasoning, language, perception, methods in usability testing, special computational techniques such as intelligent and adaptive interfaces, and design for people with cognitive disabilities. Students conduct analyses of real world problems of their own choosing and redesign/analyze a project of an interactive system. Limited enrollment seminar taught in two sections of approximately ten students each. Admission to the course is by application to the instructor, with preference given to Symbolic Systems students of advanced standing. Recommended: a course in cognitive psychology or cognitive anthropology.

SYMSYS 255. Building Digital History: Informatics of Social Movements and Protest. 3-5 Units.

A participatory course focused on the online representation of oral and archival history research. This year's thematic focus is the design and evaluation of history websites focused on social movements and protest. We will survey the field of digital history and its application to social movement research and teaching. The course will utilize materials developed in the 2014 version of the course, which focused on the history of student activism at Stanford. Class will apply lessons from digital history practice and theory to the design of an online repository and community for the collaborative representation and discussion of social movement history at Stanford, and to the further development of source material in a future version of the class. Topics will include participatory design, studies of historical learning, archiving issues, data integrity, and fair representation of different viewpoints, among others.

SYMSYS 255A. Building Digital History: Social Movements and Protest at Stanford. 1 Unit.

Lectures-only version of SYMSYS 255.

SYMSYS 271. Group Democracy. 2-4 Units.

This seminar will explore theoretical, empirical, and practical approaches to groups that come together around a common purpose or interest. Emphasis is on democratically structured, non-hierarchical and non-institutional decision making, e.g. by grassroots activists, student, or neighborhood organizations. Parliamentary, consensus, and informal procedures. How do groups form? How do they deliberate and make decision? What are the principles underlying different models for group process, and how well do different procedures work in practice? How do culture and identity affect the working of a group? And how are social technologies used? Readings from different disciplines and perspectives. Course is limited to 20 students. Prerequisite: A course in social psychology, decision making or group sociology. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit.

SYMSYS 275. Collective Behavior and Distributed Intelligence. 3 Units.

This course will explore possibilities for student research projects based on presentations of faculty research. We will cover a broad range of topics within the general area of collective behavior, both natural and artificial. Students will build on faculty presentations to develop proposals for future projects.
Same as: BIO 175

SYMSYS 280. Symbolic Systems Research Seminar. 1 Unit.

A mixture of public lectures of interest to Symbolic Systems students (the Symbolic Systems Forum) and student-led meetings to discuss research in Symbolic Systems. Can be repeated for credit. Open to both undergraduates and Master's students.

SYMSYS 290. Master's Degree Project. 1-15 Unit.

.

SYMSYS 291. Master's Program Seminar. 1 Unit.

Enrollment limited to students in the Symbolic Systems M.S. degree program. May be repeated for credit.

SYMSYS 295D. Research in Digital Democracy. 3-4 Units.

Digital democracy refers to social activity that is organized democratically at a group, institutional, or societal level, and that takes place within or is augmented by digital technology. This is a project-based research seminar designed to teach students methods for studying digital democracy, as well as collaborating in a group, the organization of a research project, and academic writing. The first few weeks of the course will be an overview of digital democracy research and its methods, as well as a time for students to organize into a group research project, The remainder of the class (about 7 weeks) will be spent performing and writing up the research for a targeted publication venue. Application required for enrollment. Prerequisite: At least one course in empirical methods or statistics. Prerequisites: At least one course in empirical methods or statistics.
Same as: SYMSYS 195D

SYMSYS 296. Independent Study. 1-15 Unit.

Independent work under the supervision of a faculty member. Can be repeated for credit.

SYMSYS 297. Teaching in Symbolic Systems. 1-5 Unit.

Leading sections, grading, and/or other duties of teaching or helping to teach a course in Symbolic Systems. Sign up with the instructor supervising the course in which you are teaching or assisting.

SYMSYS 298. Peer Advising in Symbolic Systems: Practicum. 1-2 Unit.

Optional for students selected as Undergraduate Advising Fellows in the Symbolic Systems Program. AFs work with program administrators to assist undergraduates in the Symbolic Systems major or minor, in course selection, degree planning, and relating the curriculum to a career or life plan, through advising and events. Meeting with all AFs for an hour once per week under the direction of the Associate Director. Requires a short reflective paper at the end of the quarter on what the AF has learned about advising students in the program. Repeatable for credit. May not be taken by students who receive monetary compensation for their work as an AF.

SYMSYS 299. Curricular Practical Training. 1 Unit.

Students obtain employment in a relevant research or industrial activity to enhance their professional experience consistent with their degree programs. Meets the requirements for curricular practical training for students on F-1 visas. Students submit a concise report detailing work activities, problems worked on, and key results. May be repeated for credit. Prerequisite: qualified offer of employment and consent of advisor.