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Office: Margaret Jacks Hall, Building 460, Suite 040
Mail Code: 94305-2150
Phone: (650) 723-4284
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 leading to a B.S. in Symbolic Systems provides students with a core of concepts and techniques, drawing on faculty and courses from various departments. The curriculum prepares students for advanced training in the interdisciplinary study of language and information, or for postgraduate study in any of the main contributing disciplines. It is also excellent preparation for employment immediately after graduation.

Symbolic Systems majors must complete a core of required courses plus a field of study consisting of five additional courses. All major courses are to be taken for letter grades unless an approved course is offered satisfactory/no credit only. All core courses must be passed with a grade of 'C-' or better. Students who receive a grade lower than this in a core course must alert the program of this fact so that a decision can be made about whether the student should continue in the major.

Core Requirements

In order to graduate with a B.S. in Symbolic Systems, a student must complete the following requirements. Some of these courses have other courses as prerequisites; students are responsible for completing each course's prerequisites before they take it. With the exception of the advanced small seminar requirement, courses cannot be used towards more than one area of the core requirements.  For additional information, see the Symbolic Systems web siteNote: Students matriculating in the Class of 2018 or later must take SYMSYS 1 Minds and Machines (formerly SYMSYS 100) before their declaration of the Symbolic Systems undergraduate major can be approved.

1. Introductory Core Course

Students matriculating in the Class of 2018 or later must take SYMSYS 1 Minds and Machines  (formerly SYMSYS 100) before their declaration of the Symbolic Systems undergraduate major can be approved.

Units
SYMSYS 1Minds and Machines (formerly SYMSYS 100)4

2. Continuous Fundamentals Level 1—Single Variable Calculus

Units
Select one of the following Series:
Series A
10 units of Advanced Placement Calculus credit10
Series B
MATH 19
MATH 20
MATH 21
Calculus
and Calculus
and Calculus
10
Series C
Equivalent preparation in Single Variable Calculus, as judged by student

3. Continuous Fundamentals Level 2—Multivariable Calculus

Units
Select one of the following: 1
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

4. Continuous Fundamentals Level 3—Probability and Statistics

Units
Select one of the following:
CS 109Introduction to Probability for Computer Scientists3-5
STATS 110Statistical Methods in Engineering and the Physical Sciences4-5
STATS 116Theory of Probability3-5
EE 178Probabilistic Systems Analysis4
MS&E 120Probabilistic Analysis5
CME 106/ENGR 155CIntroduction to Probability and Statistics for Engineers4

5. Discrete Fundamentals

Units
a. Computing Level 13-5
Select one of the following:
CS 106AProgramming Methodology3-5
CS 106APProgramming Methodology in Python3-5
Or equivalent preparation, as judged by student
b. Computing Level 2 3-5
Select one of the following:
CS 106BProgramming Abstractions3-5
CS 106XProgramming Abstractions (Accelerated)3-5
c. Logic and Computational Theory3-5
Select one of the following:
CS 103Mathematical Foundations of Computing3-5
PHIL 150Mathematical Logic4

6. Technical Depth

Two courses chosen from the list below (from either the same or different areas), appropriate to a student’s concentration. Students concentrating in HCI, AI, or Computer Music must take CS 107 Computer Organization and Systems. Other concentrations may also restrict the particular courses that can be taken to fulfill this requirement.

Units
Area A. Computer Programming
CS 107Computer Organization and Systems (required for HCI, AI, or Computer Music)3-5
CS 107EComputer Systems from the Ground Up3-5
Area B. Computational Theory
CS 154Introduction to Automata and Complexity Theory3-4
CS 161Design and Analysis of Algorithms3-5
PHIL 151ARecursion Theory4
Area C. Logic
CS 157Computational Logic3
PHIL 151Metalogic4
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
Area D. Decision Theory/Game Theory
CS 238Decision Making under Uncertainty3-4
ECON 160Game Theory and Economic Applications5
ECON 180Honors Game Theory5
MS&E 252Decision Analysis I: Foundations of Decision Analysis3-4
Area E. Probability and Statistics
STATS 200Introduction to Statistical Inference3
STATS 217Introduction to Stochastic Processes I2-3
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 246Mining Massive Data Sets3-4
MS&E 221Stochastic Modeling3

7. Philosophical Foundations Level 1

Units
Introductory Philosophy3-5
Select one of the following:
PHIL 1Introduction to Philosophy5
PHIL 2Introduction to Moral Philosophy5
PHIL 60Introduction to Philosophy of Science5
PHIL 102Modern Philosophy, Descartes to Kant4
PHIL 135Existentialism4
THINK 24Evil4
ESF 7Education as Self-Fashioning: The Transformation of the Self7
All 3 of the following (must complete entire sequence):
Structured Liberal Education
and Structured Liberal Education
and Structured Liberal Education
Other introductory courses taught in the Philosophy Department, if approved by the Program Director or Associate Director

8. Philosophical Foundations Level 2

Units
PHIL 80Mind, Matter, and Meaning5

9. Philosophical Foundations Level 3

Units
Select one of the following advanced undergraduate course in metaphysics/epistemology (post-PHIL 80):
PHIL 107BPlato's Later Metaphysics and Epistemology4
PHIL 167DPhilosophy of Neuroscience4
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 184Epistemology4
PHIL 186Philosophy of Mind4
PHIL 187Philosophy of Action4

Note: Symbolic Systems majors must take PHIL 182 for 3 or more units.

10. Cognition and Neuroscience

Units
Introductory Cognition and Neuroscience
PSYCH 45Introduction to Learning and Memory3
PSYCH 50Introduction to Cognitive Neuroscience4
An additional undergraduate course in cognition and/or neurosciences
Select one of the following:
BIO 150Human Behavioral Biology5
HUMBIO 3BBehavior, Health, and Development5
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 141Cognitive Development3
PSYCH 154Judgment and Decision-Making3

11. Natural Language

Units
Language and Mind 4
Select one of the following:
LINGUIST 1Introduction to Linguistics4
LINGUIST 61SLanguage Evolution and Change2-3
LINGUIST 67SThe Role of Language in Perception and Cognition3
LINGUIST 140Learning to Speak: An Introduction to Child Language Acquisition4
Linguistic Theory4
Select one of the following:
LINGUIST 105Phonetics4
LINGUIST 110Introduction to Phonology4
LINGUIST 112Seminar in Phonology: Stress, Tone, and Accent4
LINGUIST 120Introduction to Syntax4
LINGUIST 121AThe Syntax of English4
LINGUIST 121BCrosslinguistic Syntax4
LINGUIST 130A/230AIntroduction to Semantics and Pragmatics4
LINGUIST 130BIntroduction to Lexical Semantics3-4
LINGUIST 1844
LINGUIST 281Computational Models of Linguistic Formalism1-4

12. Computation and Cognition

Units
A course applying core technical skills to cognition
Select one of the following:
CS 131Computer Vision: Foundations and Applications3-4
CS 221Artificial Intelligence: Principles and Techniques3-4
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 229Machine Learning3-4
CS 230Deep Learning3-4
CS 234Reinforcement Learning3
EE 104Introduction to Machine Learning3-5
LINGUIST 180/CS 124From Languages to Information3-4
LINGUIST 182Computational Theories of Syntax3-4
NENS 220Computational Neuroscience4
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 209Neural Network Models of Cognition4
PSYCH 242Theoretical Neuroscience3
PSYCH 249Large-Scale Neural Network Modeling for Neuroscience3

Advanced Small Seminar Requirement

An upper-division, limited-enrollment seminar drawing on material from other courses in the core. Courses listed under Symbolic Systems Program offerings with numbers from SYMSYS 200 through SYMSYS 209 are acceptable, as are other courses as listed in the Advanced Small Seminar section of the Symbolic Systems website. Total enrollment must not exceed 20 students for a course to be approved as fulfilling the Advanced Small Seminar Requirement. A course taken to fulfill this requirement can also be counted toward another requirement, as part of either the core or a student's concentration, but not both.

Fields of Study

In addition to the core requirements listed above, the Symbolic Systems major requires each student to complete a field of study consisting of five courses that are thematically related to each other. Students select concentrations from the list below or design others in consultation with their advisers. The field of study is declared on Axess; it appears on the transcript but not on the diploma.

  • Applied Logic
  • Artificial Intelligence
  • Cognitive Science
  • Computer Music
  • Decision Making and Rationality
  • Human-Computer Interaction
  • Learning
  • Natural Language
  • Neurosciences
  • Philosophical Foundations

Note: A course may not count toward both a core and a concentration requirement, unless it is applied to the Advanced Small Seminar area within the core. A course that is applied to the Advanced Small Seminar requirement may also be counted toward a student's concentration or toward another core requirement, if appropriate, but not to both. 

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.

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.

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.

Option 1

One course in each of the following core areas (please note that several of these courses have prerequisites):

Units
a. Cognition
Select one of the following:
SYMSYS 1Minds and Machines (formerly SYMSYS 100)4
PSYCH 45Introduction to Learning and Memory3
PSYCH 50Introduction to Cognitive Neuroscience4
b. Logic and Computation
Select one of the following:
PHIL 150Mathematical Logic4
PHIL 150ELogic in Action: A New Introduction to Logic4
PHIL 151Metalogic4
CS 103Mathematical Foundations of Computing3-5
c. Computer Programming
Select one of the following:
CS 106BProgramming Abstractions3-5
CS 106XProgramming Abstractions (Accelerated)3-5
CS 107Computer Organization and Systems3-5
d. Philosophical Foundations
Select one of the following:
SYMSYS 1Minds and Machines (formerly SYMSYS 100)4
PHIL 80Mind, Matter, and Meaning5
e. Linguistic Theory
Select one of the following:
LINGUIST 105Phonetics4
LINGUIST 110Introduction to Phonology4
LINGUIST 120Introduction to Syntax4
LINGUIST 121AThe Syntax of English4
LINGUIST 121BCrosslinguistic Syntax4
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 130BIntroduction to Lexical Semantics3-4
LINGUIST 1844
f. Computation and Cognition3-4
Select one of the following:
CS 221Artificial Intelligence: Principles and Techniques3-4
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 229Machine Learning3-4
LINGUIST 180From Languages to Information3-4
LINGUIST 182Computational Theories of Syntax3-4
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 209Neural Network Models of Cognition4
PSYCH 239Formal and Computational Approaches in Psychology and Cognitive Science3

Option 2

SYMSYS 1 Minds and Machines (formerly SYMSYS 100), plus 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.

Coterminal Master's Degrees in Symbolic Systems

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 adviser 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.

Degree Requirements

A candidate for the M.S. degree in Symbolic Systems must complete a program of 45 units. 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 are waived only if evidence is provided that similar or more advanced courses have been taken, either at Stanford or another institution. Courses that are waived rather than taken may not be counted toward the 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 Graduate Studies Director in consultation with the student's adviser and designed to support a student's project. 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, to be modified at the time of the Project Area Statement with the approval of a student's adviser and the Graduate Studies Director. The final version of the Program Proposal, which should specify all the courses the student has taken and proposes as fulfillment of the 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). The plan of study 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: Computer Science, Linguistics, Philosophy, and Psychology. More advanced courses in each of the 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 121A, 121B or 130A. The courses below are examples of those that have been approved. Other courses may be approved if appropriate.

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 148 Introduction to Computer Graphics and Imaging
  • CS 221 Artificial Intelligence: Principles and Techniques
  • CS 224N Natural Language Processing with Deep Learning
  • CS 224W Analysis of Networks

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.

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

4. Completion of a substantial project appropriate to the program plan, represented by the M.S. Thesis, the last of the the M.S research documents. The project normally takes three quarters, and work on the project may account for up to 15 units of a student's 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 copy of the thesis must be submitted (in both print and electronic forms) to the Associate Director of Symbolic Systems, with the print version 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.

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.

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

Faculty

Director: Kenneth A. Taylor

Director of Graduate Studies: Kenneth A. Taylor

Associate Director: Todd Davies

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

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), David Dill (Professor, emeritus), 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), Nils Nilsson (Professor, emeritus), Vaughan Pratt (Professor, emeritus), Eric Roberts (Professor, emeritus), Mehran Sahami (Professor, Teaching), Yoav Shoham (Professor, emeritus), Sebastian Thrun (Adjunct Professor), Terry Winograd (Professor, emeritus)

Economics: Muriel Niederle (Professor)

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

Electrical Engineering: Krishna Shenoy (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)

Linguistics:  Arto Anttila (Associate Professor), Joan Bresnan (Professor, emerita), Eve Clark (Professor, emerita), Cleo Condoravdi (Professor Research),  Judith Degen (Assistant Professor), Penelope Eckert (Professor), Daniel Jurafsky (Professor), Ronald Kaplan (Adjunct Professor), Lauri Karttunen (Adjunct Professor), Martin Kay (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)

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), 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), Kenneth Taylor (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)

Cognate Courses for the Bachelor's Degree

The following is a list of cognate courses that may be applied to the B.S. in Symbolic Systems. Click on the course or see ExploreCourses for course descriptions and General Education Requirements (GER) information. Courses taken for a Symbolic Systems degree or Minor must be taken for 3 units (or more). See Degree Requirements for details.

Core

Units
APPPHYS 293Theoretical Neuroscience3
BIO 150Human Behavioral Biology5
CME 100Vector Calculus for Engineers5
CME 100AVector Calculus for Engineers, ACE6
CME 106Introduction to Probability and Statistics for Engineers4
CS 103Mathematical Foundations of Computing3-5
CS 106AProgramming Methodology3-5
CS 106BProgramming Abstractions3-5
CS 106XProgramming Abstractions (Accelerated)3-5
CS 107Computer Organization and Systems3-5
CS 107EComputer Systems from the Ground Up3-5
CS 109Introduction to Probability for Computer Scientists3-5
CS 124From Languages to Information3-4
CS 131Computer Vision: Foundations and Applications3-4
CS 154Introduction to Automata and Complexity Theory3-4
CS 157Computational Logic3
CS 161Design and Analysis of Algorithms3-5
CS 221Artificial Intelligence: Principles and Techniques3-4
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 229Machine Learning3-4
CS 238Decision Making under Uncertainty3-4
CS 246Mining Massive Data Sets3-4
ECON 160Game Theory and Economic Applications5
ECON 180Honors Game Theory5
EE 178Probabilistic Systems Analysis4
ENGR 155CIntroduction to Probability and Statistics for Engineers4
ESF 7Education as Self-Fashioning: The Transformation of the Self7
ETHICSOC 20Introduction to Moral Philosophy5
HUMBIO 3BBehavior, Health, and Development5
LINGUIST 1Introduction to Linguistics4
LINGUIST 105Phonetics4
LINGUIST 106Introduction to Speech Perception4
LINGUIST 110Introduction to Phonology4
LINGUIST 120Introduction to Syntax4
LINGUIST 121AThe Syntax of English4
LINGUIST 121BCrosslinguistic Syntax4
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 130BIntroduction to Lexical Semantics3-4
LINGUIST 140Learning to Speak: An Introduction to Child Language Acquisition4
LINGUIST 180From Languages to Information3-4
LINGUIST 182Computational Theories of Syntax3-4
LINGUIST 230AIntroduction to Semantics and Pragmatics4
LINGUIST 281Computational Models of Linguistic Formalism1-4
LINGUIST 282Computational Theories of Syntax3-4
MATH 19Calculus3
MATH 20Calculus3
MATH 21Calculus4
MATH 51Linear Algebra, Multivariable Calculus, and Modern Applications5
MATH 51ALinear Algebra, Multivariable Calculus, and Modern Applications, ACE6
MATH 151Introduction to Probability Theory3
MS&E 120Probabilistic Analysis5
MS&E 220Probabilistic Analysis3-4
MS&E 221Stochastic Modeling3
MS&E 252Decision Analysis I: Foundations of Decision Analysis3-4
NENS 220Computational Neuroscience4
PHIL 1Introduction to Philosophy5
PHIL 2Introduction to Moral Philosophy5
PHIL 60Introduction to Philosophy of Science5
PHIL 80Mind, Matter, and Meaning5
PHIL 102Modern Philosophy, Descartes to Kant4
PHIL 107BPlato's Later Metaphysics and Epistemology4
PHIL 135Existentialism4
PHIL 150Mathematical Logic4
PHIL 150ELogic in Action: A New Introduction to Logic4
PHIL 151Metalogic4
PHIL 151ARecursion Theory4
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
PHIL 162Philosophy of Mathematics4
PHIL 164Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 166Probability: Ten Great Ideas About Chance4
PHIL 167BPhilosophy, Biology, and Behavior4
PHIL 169Evolution of the Social Contract4
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 184Epistemology4
PHIL 184FFeminist Theories of Knowledge4
PHIL 184PProbability and Epistemology4
PHIL 185Theory of Understanding4
PHIL 186Philosophy of Mind4
PHIL 187Philosophy of Action4
PHIL 188Personal Identity4
PHIL 189Examples of Free Will4
PHIL 280ARealism, Anti-Realism, Irrealism, Quasi-Realism4
PHIL 289Examples of Free Will4
POLISCI 152Introduction to Game Theoretic Methods in Political Science3-5
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 141Cognitive Development3
PSYCH 154Judgment and Decision-Making3
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 209Neural Network Models of Cognition4
PSYCH 239Formal and Computational Approaches in Psychology and Cognitive Science3
SLE 91Structured Liberal Education8
SLE 92Structured Liberal Education8
SLE 93Structured Liberal Education8
STATS 110Statistical Methods in Engineering and the Physical Sciences4-5
STATS 116Theory of Probability3-5
STATS 200Introduction to Statistical Inference3
STATS 217Introduction to Stochastic Processes I2-3
THINK 24Evil4

Note: Symbolic Systems majors must take PHIL 182 Advanced Philosophy of Language for 3 or more units.

Artificial Intelligence

Units
CS 124From Languages to Information3-4
CS 154Introduction to Automata and Complexity Theory3-4
CS 157Computational Logic3
CS 223AIntroduction to Robotics3
CS 224NNatural Language Processing with Deep Learning3-4
CS 224SSpoken Language Processing2-4
CS 224UNatural Language Understanding3-4
CS 225AExperimental Robotics3
CS 227BGeneral Game Playing3
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 229Machine Learning3-4
CS 270Modeling Biomedical Systems: Ontology, Terminology, Problem Solving3
CS 274Representations and Algorithms for Computational Molecular Biology3-4
CS 329Topics in Artificial Intelligence3
ECON 160Game Theory and Economic Applications5
EE 263Introduction to Linear Dynamical Systems3
EE 364AConvex Optimization I3
EE 364BConvex Optimization II3
EE 376AInformation Theory3
EE 376BNetwork Information Theory3
ENGR 205Introduction to Control Design Techniques3
ENGR 209AAnalysis and Control of Nonlinear Systems3
LINGUIST 180From Languages to Information3-4
LINGUIST 188Natural Language Understanding3-4
LINGUIST 280From Languages to Information3-4
LINGUIST 284Natural Language Processing with Deep Learning3-4
LINGUIST 285Spoken Language Processing2-4
LINGUIST 288Natural Language Understanding3-4
MATH 113Linear Algebra and Matrix Theory3
MS&E 251Introduction to Stochastic Control with Applications3
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
STATS 315AModern Applied Statistics: Learning2-3
STATS 315BModern Applied Statistics: Data Mining2-3

Applied Logic

Units
CS 154Introduction to Automata and Complexity Theory3-4
CS 157Computational Logic3
LINGUIST 230AIntroduction to Semantics and Pragmatics4
MATH 161Set Theory3
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
PHIL 155General Interest Topics in Mathematical Logic4
PHIL 350AModel Theory3
PHIL 351ARecursion Theory3
PHIL 354Topics in Logic1-3
PHIL 391Research Seminar in Logic1-3

Philosophical Foundations

Units
LINGUIST 110Introduction to Phonology4
PHIL 14NBelief and the Will3
PHIL 102Modern Philosophy, Descartes to Kant4
PHIL 143Quine4
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
PHIL 157Topics in Philosophy of Logic3
PHIL 162Philosophy of Mathematics4
PHIL 164Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 165Philosophy of Physics: Philosophical Issues in Quantum Mechanics4
PHIL 166Probability: Ten Great Ideas About Chance4
PHIL 167BPhilosophy, Biology, and Behavior4
PHIL 170Ethical Theory4
PHIL 180Metaphysics4
PHIL 180ARealism, Anti-Realism, Irrealism, Quasi-Realism4
PHIL 181Philosophy of Language4
PHIL 184Epistemology4
PHIL 184PProbability and Epistemology4
PHIL 252Computability and Logic4
PHIL 254Modal Logic4
PHIL 264Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 265Philosophy of Physics: Philosophical Issues in Quantum Mechanics4
PHIL 266Probability: Ten Great Ideas About Chance4
PHIL 267BPhilosophy, Biology, and Behavior4
PHIL 280ARealism, Anti-Realism, Irrealism, Quasi-Realism4
PHIL 350AModel Theory3

Cognitive Science

Units
BIO 150Human Behavioral Biology5
COMM 106Communication Research Methods4-5
CS 124From Languages to Information3-4
CS 154Introduction to Automata and Complexity Theory3-4
CS 224NNatural Language Processing with Deep Learning3-4
CS 229Machine Learning3-4
ECON 160Game Theory and Economic Applications5
EE 376AInformation Theory3
EE 376BNetwork Information Theory3
HUMBIO 160Human Behavioral Biology5
LINGUIST 105Phonetics4
LINGUIST 110Introduction to Phonology4
LINGUIST 140Learning to Speak: An Introduction to Child Language Acquisition4
LINGUIST 180From Languages to Information3-4
LINGUIST 205APhonetics4
LINGUIST 241Language Acquisition II4
LINGUIST 247Seminar in Psycholinguistics: Advanced Topics2-4
LINGUIST 280From Languages to Information3-4
LINGUIST 284Natural Language Processing with Deep Learning3-4
MATH 113Linear Algebra and Matrix Theory3
MUSIC 251Psychophysics and Music Cognition1-5
NBIO 206The Nervous System6
NBIO 218Neural Basis of Behavior5
PHIL 152Computability and Logic4
PHIL 154Modal Logic4
PHIL 164Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 180Metaphysics4
PHIL 180ARealism, Anti-Realism, Irrealism, Quasi-Realism4
PHIL 181Philosophy of Language4
PHIL 184Epistemology4
PHIL 184PProbability and Epistemology4
PHIL 186Philosophy of Mind4
PHIL 187Philosophy of Action4
PHIL 188Personal Identity4
PHIL 189Examples of Free Will4
PHIL 264Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 280ARealism, Anti-Realism, Irrealism, Quasi-Realism4
PHIL 289Examples of Free Will4
PSYCH 1Introduction to Psychology5
PSYCH 30Introduction to Perception4
PSYCH 45Introduction to Learning and Memory3
PSYCH 50Introduction to Cognitive Neuroscience4
PSYCH 70Self and Society: Introduction to Social Psychology4
PSYCH 75Introduction to Cultural Psychology5
PSYCH 141Cognitive Development3
PSYCH 154Judgment and Decision-Making3
PSYCH 202Cognitive Neuroscience3
PSYCH 204AHuman Neuroimaging Methods3
PSYCH 204BComputational Neuroimaging: Methods & Analyses1-3
PSYCH 205Foundations of Cognition3
PSYCH 221Image Systems Engineering1-3
PSYCH 227Seminar in Psycholinguistics: Advanced Topics2-4
PSYCH 232Brain and Decision3
PSYCH 250High-level Vision: From Neurons to Deep Neural Networks1-3
PSYCH 252Statistical Methods for Behavioral and Social Sciences1-6
PSYCH 279Topics in Cognitive Control1-3
STATS 191Introduction to Applied Statistics3-4
STATS 200Introduction to Statistical Inference3

Decision Making and Rationality

Units
BIO 150Human Behavioral Biology5
BIOMEDIN 251Outcomes Analysis4
COMM 106Communication Research Methods4-5
COMM 172Media Psychology4-5
COMM 206Communication Research Methods4-5
COMM 272Media Psychology4-5
CS 147Introduction to Human-Computer Interaction Design3-5
CS 154Introduction to Automata and Complexity Theory3-4
CS 161Design and Analysis of Algorithms3-5
CS 181Computers, Ethics, and Public Policy4
CS 204Legal Informatics2-3
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 261Optimization and Algorithmic Paradigms3
ECON 50Economic Analysis I5
ECON 51Economic Analysis II5
ECON 102BApplied Econometrics5
ECON 102CAdvanced Topics in Econometrics5
ECON 136Market Design5
ECON 137Decision Modeling and Information5
ECON 141Public Finance and Fiscal Policy5
ECON 150Economic Policy Analysis4-5
ECON 155Environmental Economics and Policy5
ECON 160Game Theory and Economic Applications5
ECON 179Experimental Economics5
ECON 286Game Theory and Economic Applications2-5
ECON 288Computational Economics2-5
ECON 289Advanced Topics in Game Theory and Information Economics2-5
ECON 290Multiperson Decision Theory3
EDUC 247Moral and Character Education3
EDUC 375ASeminar on Organizational Theory5
ENGR 62Introduction to Optimization3-4
MS&E 111Introduction to Optimization3-4
MS&E 120Probabilistic Analysis5
MS&E 121Introduction to Stochastic Modeling4
MS&E 180Organizations: Theory and Management4
MS&E 201Dynamic Systems3-4
MS&E 250AEngineering Risk Analysis3
MS&E 250BProject Course in Engineering Risk Analysis3
MS&E 252Decision Analysis I: Foundations of Decision Analysis3-4
MS&E 254The Ethical Analyst1-3
MS&E 299Voluntary Social Systems1-3
MS&E 352Decision Analysis II: Professional Decision Analysis3-4
MS&E 355Influence Diagrams and Probabilistics Networks3
PHIL 154Modal Logic4
PHIL 164Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 166Probability: Ten Great Ideas About Chance4
PHIL 167BPhilosophy, Biology, and Behavior4
PHIL 170Ethical Theory4
PHIL 194CTime and Free Will4
PHIL 194REpistemic Paradoxes4
PHIL 264Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 266Probability: Ten Great Ideas About Chance4
PHIL 267BPhilosophy, Biology, and Behavior4
PHIL 270Ethical Theory4
PHIL 355Logic and Social Choice4
PHIL 366Evolution and Communication4
PHIL 387Intention and Normative Judgment2-4
POLISCI 152Introduction to Game Theoretic Methods in Political Science3-5
POLISCI 344UPolitical Culture3-5
POLISCI 351AFoundations of Political Economy3
PSYCH 45Introduction to Learning and Memory3
PSYCH 50Introduction to Cognitive Neuroscience4
PSYCH 70Self and Society: Introduction to Social Psychology4
PSYCH 75Introduction to Cultural Psychology5
PSYCH 80Introduction to Personality and Affective Science3
PSYCH 154Judgment and Decision-Making3
PSYCH 168Emotion Regulation3
PSYCH 205Foundations of Cognition3
PSYCH 212Classic and contemporary social psychology research1-3
PSYCH 215Mind, Culture, and Society3
PSYCH 223Social Norms3
PSYCH 232Brain and Decision3
PSYCH 252Statistical Methods for Behavioral and Social Sciences1-6
PSYCH 253High-Dimensional Methods for Behavioral and Neural Data3
PSYCH 268Emotion Regulation3
PSYCH 279Topics in Cognitive Control1-3
PUBLPOL 302BEconomic Analysis of Law3
SOC 114Economic Sociology4
SOC 115Topics in Economic Sociology5
SOC 120Interpersonal Relations4
SOC 121The Individual in Social Structure: Foundations in Sociological Social Psychology5
SOC 126Introduction to Social Networks4
SOC 127Bargaining, Power, and Influence in Social Interaction5
SOC 160Formal Organizations4
SOC 214Economic Sociology4
SOC 220Interpersonal Relations4
SOC 226Introduction to Social Networks4
SOC 227Bargaining, Power, and Influence in Social Interaction5
SOC 260Formal Organizations4
STATS 200Introduction to Statistical Inference3
STATS 211Meta-research: Appraising Research Findings, Bias, and Meta-analysis3
STATS 217Introduction to Stochastic Processes I2-3
STATS 218Introduction to Stochastic Processes II3
STATS 310ATheory of Probability I2-4
STATS 310BTheory of Probability II2-3
STATS 310CTheory of Probability III2-4
SYMSYS Majors must take for 3 or more units

Natural Language

Units
CS 124From Languages to Information3-4
CS 154Introduction to Automata and Complexity Theory3-4
CS 224NNatural Language Processing with Deep Learning3-4
CS 224SSpoken Language Processing2-4
CS 224UNatural Language Understanding3-4
CS 229Machine Learning3-4
CS 276Information Retrieval and Web Search3
LINGUIST 105Phonetics4
LINGUIST 110Introduction to Phonology4
LINGUIST 116Morphology4
LINGUIST 130AIntroduction to Semantics and Pragmatics4
LINGUIST 130BIntroduction to Lexical Semantics3-4
LINGUIST 140Learning to Speak: An Introduction to Child Language Acquisition4
LINGUIST 180From Languages to Information3-4
LINGUIST 188Natural Language Understanding3-4
LINGUIST 205BAdvanced Phonetics2-4
LINGUIST 210APhonology3-4
LINGUIST 210BAdvanced Phonology2-4
LINGUIST 221AFoundations of English Grammar1-4
LINGUIST 221BStudies in Universal Grammar1-4
LINGUIST 222AFoundations of Syntactic Theory I3-4
LINGUIST 224BAdvanced Topics in Lexical Functional Grammar1-4
LINGUIST 230AIntroduction to Semantics and Pragmatics4
LINGUIST 230BSemantics and Pragmatics I2-4
LINGUIST 232ALexical Semantics2-4
LINGUIST 241Language Acquisition II4
LINGUIST 247Seminar in Psycholinguistics: Advanced Topics2-4
LINGUIST 280From Languages to Information3-4
LINGUIST 281Computational Models of Linguistic Formalism1-4
LINGUIST 285Spoken Language Processing2-4
LINGUIST 286Information Retrieval and Web Search3
LINGUIST 288Natural Language Understanding3-4
PHIL 154Modal Logic4
PHIL 181Philosophy of Language4
PSYCH 227Seminar in Psycholinguistics: Advanced Topics2-4

Learning

Units
EDUC 230Learning Experience Design3
CS 147Introduction to Human-Computer Interaction Design3-5
CS 224NNatural Language Processing with Deep Learning3-4
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 229Machine Learning3-4
EDUC 124Collaborative Design and Research of Technology-integrated Curriculum3-4
EDUC 218Topics in Cognition and Learning: Technology and Multitasking3
EDUC 333AUnderstanding Learning Environments3
EDUC 342Child Development and New Technologies3
EE 376AInformation Theory3
LINGUIST 140Learning to Speak: An Introduction to Child Language Acquisition4
LINGUIST 241Language Acquisition II4
LINGUIST 284Natural Language Processing with Deep Learning3-4
PSYCH 7QLanguage Understanding by Children and Adults3
PSYCH 45Introduction to Learning and Memory3
PSYCH 50Introduction to Cognitive Neuroscience4
PSYCH 141Cognitive Development3
PSYCH 202Cognitive Neuroscience3
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 239Formal and Computational Approaches in Psychology and Cognitive Science3
STATS 315AModern Applied Statistics: Learning2-3
STATS 315BModern Applied Statistics: Data Mining2-3

Neurosciences

Units
BIO 150Human Behavioral Biology5
BIO 153Cellular Neuroscience: Cell Signaling and Behavior4
BIO 154Molecular and Cellular Neurobiology4
BIO 158Developmental Neurobiology4
BIO 222Exploring Neural Circuits3
CS 223AIntroduction to Robotics3
CS 229Machine Learning3-4
MATH 113Linear Algebra and Matrix Theory3
NBIO 206The Nervous System6
NBIO 218Neural Basis of Behavior5
NENS 220Computational Neuroscience4
PHIL 186Philosophy of Mind4
PSYCH 30Introduction to Perception4
PSYCH 45Introduction to Learning and Memory3
PSYCH 50Introduction to Cognitive Neuroscience4
PSYCH 120Cellular Neuroscience: Cell Signaling and Behavior4
PSYCH 121Ion Transport and Intracellular Messengers3
PSYCH 204AHuman Neuroimaging Methods3
PSYCH 204BComputational Neuroimaging: Methods & Analyses1-3
PSYCH 221Image Systems Engineering1-3
PSYCH 232Brain and Decision3
PSYCH 250High-level Vision: From Neurons to Deep Neural Networks1-3
PSYCH 252Statistical Methods for Behavioral and Social Sciences1-6
PSYCH 279Topics in Cognitive Control1-3
STATS 141Biostatistics3-5
STATS 191Introduction to Applied Statistics3-4
STATS 200Introduction to Statistical Inference3

Cognate Courses for the Master's Degree

The following is a list of cognate courses that may be applied to the M.S. in Symbolic Systems. Click on the course or see ExploreCourses for course descriptions and General Education Requirements (GER) information. Courses taken for a Symbolic Systems degree or Minor must be taken for 3 units (or more). See Degree Requirements for details.

Units
BIO 153Cellular Neuroscience: Cell Signaling and Behavior4
BIO 154Molecular and Cellular Neurobiology4
BIO 222Exploring Neural Circuits3
BIO 258Developmental Neurobiology4
BIO 263Neural Systems and Behavior4
BIOMEDIN 251Outcomes Analysis4
CME 100Vector Calculus for Engineers5
CME 100AVector Calculus for Engineers, ACE6
CME 106Introduction to Probability and Statistics for Engineers4
CME 108Introduction to Scientific Computing3
COMM 206Communication Research Methods4-5
COMM 220Digital Media in Society4-5
COMM 272Media Psychology4-5
CS 103Mathematical Foundations of Computing3-5
CS 106AProgramming Methodology3-5
CS 106XProgramming Abstractions (Accelerated)3-5
CS 107Computer Organization and Systems3-5
CS 108Object-Oriented Systems Design3-4
CS 109Introduction to Probability for Computer Scientists3-5
CS 142Web Applications3
CS 147Introduction to Human-Computer Interaction Design3-5
CS 148Introduction to Computer Graphics and Imaging3-4
CS 154Introduction to Automata and Complexity Theory3-4
CS 157Computational Logic3
CS 161Design and Analysis of Algorithms3-5
CS 170Stanford Laptop Orchestra: Composition, Coding, and Performance1-5
CS 181Computers, Ethics, and Public Policy4
CS 204Legal Informatics2-3
CS 205A
CS 221Artificial Intelligence: Principles and Techniques3-4
CS 223AIntroduction to Robotics3
CS 224NNatural Language Processing with Deep Learning3-4
CS 224SSpoken Language Processing2-4
CS 224UNatural Language Understanding3-4
CS 225AExperimental Robotics3
CS 225B
CS 227BGeneral Game Playing3
CS 228Probabilistic Graphical Models: Principles and Techniques3-4
CS 229Machine Learning3-4
CS 247Human-Computer Interaction Design Studio3-4
CS 261Optimization and Algorithmic Paradigms3
CS 270Modeling Biomedical Systems: Ontology, Terminology, Problem Solving3
CS 274Representations and Algorithms for Computational Molecular Biology3-4
CS 276Information Retrieval and Web Search3
CS 294HResearch Project in Human-Computer Interaction3
CS 376Human-Computer Interaction Research3-4
CS 377Topics in Human-Computer Interaction2-3
CS 448BData Visualization3
ECON 102BApplied Econometrics5
ECON 102CAdvanced Topics in Econometrics5
ECON 135
ECON 136Market Design5
ECON 137Decision Modeling and Information5
ECON 141Public Finance and Fiscal Policy5
ECON 153
ECON 155Environmental Economics and Policy5
ECON 160Game Theory and Economic Applications5
ECON 179Experimental Economics5
ECON 190
ECON 289Advanced Topics in Game Theory and Information Economics2-5
EDUC 218Topics in Cognition and Learning: Technology and Multitasking3
EDUC 247Moral and Character Education3
EDUC 298Seminar on Teaching Introductory Computer Science1
EDUC 333AUnderstanding Learning Environments3
EDUC 342Child Development and New Technologies3
EDUC 375ASeminar on Organizational Theory5
EE 263Introduction to Linear Dynamical Systems3
EE 364AConvex Optimization I3
EE 364BConvex Optimization II3
EE 376AInformation Theory3
ENGR 155CIntroduction to Probability and Statistics for Engineers4
ENGR 205Introduction to Control Design Techniques3
ENGR 209AAnalysis and Control of Nonlinear Systems3
LINGUIST 106Introduction to Speech Perception4
LINGUIST 110Introduction to Phonology4
LINGUIST 116Morphology4
LINGUIST 120Introduction to Syntax4
LINGUIST 130BIntroduction to Lexical Semantics3-4
LINGUIST 205APhonetics4
LINGUIST 205BAdvanced Phonetics2-4
LINGUIST 210APhonology3-4
LINGUIST 210BAdvanced Phonology2-4
LINGUIST 221AFoundations of English Grammar1-4
LINGUIST 221BStudies in Universal Grammar1-4
LINGUIST 222AFoundations of Syntactic Theory I3-4
LINGUIST 224
LINGUIST 224BAdvanced Topics in Lexical Functional Grammar1-4
LINGUIST 230AIntroduction to Semantics and Pragmatics4
LINGUIST 230BSemantics and Pragmatics I2-4
LINGUIST 232ALexical Semantics2-4
LINGUIST 241Language Acquisition II4
LINGUIST 280From Languages to Information3-4
LINGUIST 281Computational Models of Linguistic Formalism1-4
LINGUIST 282Computational Theories of Syntax3-4
LINGUIST 284Natural Language Processing with Deep Learning3-4
LINGUIST 286Information Retrieval and Web Search3
LINGUIST 288Natural Language Understanding3-4
MATH 113Linear Algebra and Matrix Theory3
MATH 151Introduction to Probability Theory3
MATH 161Set Theory3
ME 115AIntroduction to Human Values in Design3
ME 115BProduct Design Methods4
MUSIC 128Stanford Laptop Orchestra: Composition, Coding, and Performance1-5
MUSIC 220AFundamentals of Computer-Generated Sound2-4
MUSIC 220BCompositional Algorithms, Psychoacoustics, and Computational Music2-4
MUSIC 220CResearch Seminar in Computer-Generated Music2-4
MUSIC 250APhysical Interaction Design for Music3-4
MUSIC 251Psychophysics and Music Cognition1-5
MUSIC 253Symbolic Musical Information2-4
MUSIC 254Music Query, Analysis, and Style Simulation2-4
NBIO 206The Nervous System6
NBIO 218Neural Basis of Behavior5
NBIO 220
NENS 220Computational Neuroscience4
PHIL 102Modern Philosophy, Descartes to Kant4
PHIL 184PProbability and Epistemology4
PHIL 185Theory of Understanding4
PHIL 194CTime and Free Will4
PHIL 194REpistemic Paradoxes4
PHIL 243Quine4
PHIL 250Mathematical Logic4
PHIL 251Metalogic4
PHIL 252Computability and Logic4
PHIL 254Modal Logic4
PHIL 257Topics in Philosophy of Logic3
PHIL 264Central Topics in the Philosophy of Science: Theory and Evidence4
PHIL 265Philosophy of Physics: Philosophical Issues in Quantum Mechanics4
PHIL 266Probability: Ten Great Ideas About Chance4
PHIL 267BPhilosophy, Biology, and Behavior4
PHIL 270Ethical Theory4
PHIL 280
PHIL 280ARealism, Anti-Realism, Irrealism, Quasi-Realism4
PHIL 281Philosophy of Language4
PHIL 282Advanced Philosophy of Language4
PHIL 284Epistemology4
PHIL 284FFeminist Theories of Knowledge4
PHIL 286Philosophy of Mind4
PHIL 287
PHIL 288Personal Identity4
PHIL 289Examples of Free Will4
PHIL 350AModel Theory3
PHIL 351ARecursion Theory3
PHIL 354Topics in Logic1-3
PHIL 355Logic and Social Choice4
PHIL 366Evolution and Communication4
PHIL 387Intention and Normative Judgment2-4
PHIL 391Research Seminar in Logic1-3
POLISCI 351AFoundations of Political Economy3
POLISCI 352Introduction to Game Theoretic Methods in Political Science3-5
PSYCH 104
PSYCH 110
PSYCH 120Cellular Neuroscience: Cell Signaling and Behavior4
PSYCH 134
PSYCH 141Cognitive Development3
PSYCH 143
PSYCH 152
PSYCH 154Judgment and Decision-Making3
PSYCH 167
PSYCH 202Cognitive Neuroscience3
PSYCH 204Computation and Cognition: The Probabilistic Approach3
PSYCH 204AHuman Neuroimaging Methods3
PSYCH 204BComputational Neuroimaging: Methods & Analyses1-3
PSYCH 205Foundations of Cognition3
PSYCH 212Classic and contemporary social psychology research1-3
PSYCH 215Mind, Culture, and Society3
PSYCH 221Image Systems Engineering1-3
PSYCH 223Social Norms3
PSYCH 226Models and Mechanisms of Memory1-3
PSYCH 228Ion Transport and Intracellular Messengers3
PSYCH 232Brain and Decision3
PSYCH 239Formal and Computational Approaches in Psychology and Cognitive Science3
PSYCH 245
PSYCH 250High-level Vision: From Neurons to Deep Neural Networks1-3
PSYCH 251Experimental Methods3
PSYCH 252Statistical Methods for Behavioral and Social Sciences1-6
PSYCH 253High-Dimensional Methods for Behavioral and Neural Data3
PSYCH 259Race and Crime3
PSYCH 262
PSYCH 270The Self: Representations and Interventions3
PSYCH 272
PSYCH 279Topics in Cognitive Control1-3
PUBLPOL 201Politics and Public Policy4-5
PUBLPOL 202
PUBLPOL 204Economic Policy Analysis4-5
PUBLPOL 302BEconomic Analysis of Law3
SOC 121The Individual in Social Structure: Foundations in Sociological Social Psychology5
SOC 214Economic Sociology4
SOC 220Interpersonal Relations4
SOC 226Introduction to Social Networks4
SOC 227Bargaining, Power, and Influence in Social Interaction5
STATS 110Statistical Methods in Engineering and the Physical Sciences4-5
STATS 116Theory of Probability3-5
STATS 141Biostatistics3-5
STATS 191Introduction to Applied Statistics3-4
STATS 200Introduction to Statistical Inference3
STATS 211Meta-research: Appraising Research Findings, Bias, and Meta-analysis3
STATS 217Introduction to Stochastic Processes I2-3
STATS 218Introduction to Stochastic Processes II3
STATS 310ATheory of Probability I2-4
STATS 310BTheory of Probability II2-3
STATS 310CTheory of Probability III2-4
STATS 315AModern Applied Statistics: Learning2-3
STATS 315BModern Applied Statistics: Data Mining2-3

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. Undergraduates considering a major in symbolic systems should take this course as early as possible in their program of study.
Same as: LINGUIST 35, PHIL 99, PSYCH 35

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 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 161. Applied Symbolic Systems: Venture Capital, Artificial Intelligence, and The Future. 2 Units.

A weekly seminar allowing students the opportunity to discuss and explore applied Symbolic Systems in technology, entrepreneurship, and venture capital. We will explore popular conventions and trends through the lens of numerous deductive and applied Symbolic Systems.
Same as: SYMSYS 261

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 196. Independent Study. 1-15 Unit.

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

SYMSYS 200. Symbolic Systems in Practice. 2-3 Units.

Applying a Symbolic Systems education at Stanford and outside. The basics of research and practice. Students develop and present a project, and investigate different career paths, including academic, industrial, professional, and public service, through interviews with alumni.

SYMSYS 201. ICT, 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 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 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 261. Applied Symbolic Systems: Venture Capital, Artificial Intelligence, and The Future. 2 Units.

A weekly seminar allowing students the opportunity to discuss and explore applied Symbolic Systems in technology, entrepreneurship, and venture capital. We will explore popular conventions and trends through the lens of numerous deductive and applied Symbolic Systems.
Same as: SYMSYS 161

SYMSYS 265. Quantum Algorithms and Quantum Cognition. 4 Units.

Quantum computers can solve some classes of problems with more efficiency than classical computers, usually exponentially faster. They have the potential to solve in minutes problems that would take for a classical computer longer than the age of the universe. Among the promising applications are the development of new drugs, and new materials, machine learning and cryptographic key breaking, just to mention a few examples. Until recently the idea of building a computer seemed like a project reserved for a distant future, but over the past years many companies such as IBM, Google, Microsoft, D-Wave, Rigetti Computing, and others have announced that they started the operation of quantum computer prototypes. However, due to the counterintuitive properties of quantum theory the creation of quantum algorithms has been as difficult as hardware development. Although there are many algorithms built to run on quantum computers there are very few that use the full potential of quantum computing. The purpose of this course is to teach the fundamentals of quantum computing and quantum algorithms for students with non-physics background. The emphasis of the course will be to develop a "quantum intuition" by presenting the main differences between classical and quantum logic, as well as the use of special examples developed in quantum cognition. Quantum cognition applies the mathematical formalism of quantum mechanics in psychology and decision theories in situations where conventional formalism does not work. The topics covered will include: the basics of quantum theory and quantum computation, Classical and Quantum Logic, Classical and Quantum gates, Quantum Cognition, the main Quantum algorithms such as Phil's Algorithm, Deutsch Algorithm, Deutsch-Jozsa Algorithm, Simon's algorithm, Shor's Algorithm, and Grover's Algorithm. This course has workshop format involving readings followed by short lectures, discussion, plus other activities in class, homework, and Final Project. Required background: linear algebra, calculus equivalent to MATH 19 and MATH 20, basic probability theory and complex numbers. Students are not expected to have taken previous courses in quantum mechanics.

SYMSYS 266. Consciousness in the Age of A.I. and Neuroscience. 3 Units.

For thousands of years consciousness has been a subject restricted to the considerations of philosophers and religious, however with advances in brain research and the possibility that in the near future we have machines with intelligence similar to that of humans has motivated efforts to build a scientific approach to the research of consciousness. Unlike other scientific research objects, consciousness has a property not present in other areas of study such as physics, chemistry, or biology. This property is subjective experience. This is an element that makes it difficult to use the traditional methods employed in science to learn what consciousness is. The purpose of this course is to introduce to students the evolution of this effort to build a proper approach for scientific research of consciousness through the suggestions/ideas of its main authors. We will present theories that philosophers and scientists have proposed in recent times. We will discuss the reasons why is important understanding what consciousness is, and discuss questions such as: Is it possible for an A.I to become aware? Is our current criterion for declaring a person to be dead correct? Can people in a coma be aware of what is happening around them? Is it possible to understand consciousness without integrating it into the study of physical reality? Prerequisite: A previous course in the philosophy of mind or consent of the instructor.

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.nFirst meeting is the second Monday of the quarter.

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

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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 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.