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This archived information is dated to the 2009-10 academic year only and may no longer be current.

For currently applicable policies and information, see the current Stanford Bulletin.

Master of Science in Symbolic Systems

The University's basic requirements for the M.S. degree is 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 "Undergraduate Degrees and Programs" section of this bulletin (see "Coterminal Bachelor's and Master's Degrees"). 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


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 to fulfill requirements A, B, or C below must be taken for a letter grade unless the course is offered S/NC only. The 45 units may include no more than 21 units of courses from those listed below under Requirements A and B. Furthermore, 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.

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


Demonstrated competence in the core requirements for the B.S. degree in Symbolic Systems. Candidates who have gone through the Symbolic Systems undergraduate program satisfy this requirement in the course of the B.S. degree in Symbolic Systems. Other students admitted as candidates for a Symbolic Systems M.S. degree must complete or show evidence of having passed equivalent courses covering all the Symbolic Systems undergraduate core requirements, with the exception of the advanced small seminar requirement.

  1. Submission to and approval by the Symbolic Systems Program office of these pre-project research documents:
    1. project area statement, endorsed with a commitment from the student's prospective project adviser due 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 two additional skill requirements:
    1. Computer Programming: CS 108. Object-Oriented Systems Design; or CS 110. Principles of Computer Systems; or CS 249A. Object-Oriented Design from a Modeling and Simulation Perspective
    2. Empirical Methods: one of the following:

    COMM 206. Communication Research Methods

    COMM 239. Questionnaire Design for Surveys and Laboratory Experiments: Social and Cognitive Perspectives

    COMM 268. Experimental Research in Advanced User Interfaces

    LINGUIST 284. Natural Language Processing (Same as CS 224N)

    PSYCH 110. Research Methods and Experimental Design

    PSYCH 252. Statistical Methods for Behavioral and Social Science (for 3 or more units)

    PSYCH 253. Statistical Theory, Models, and Methodology (for 3 units)

    STATS 191. Introduction to Applied Statistics

    STATS 200. Introduction to Statistical Inference

    a Statistics course numbered higher than 200

  3. Completion of three quarters of the Symbolic Systems Program M.S. Seminar (SYMSYS 291).

Completion of an approved specialization track. All tracks of the Symbolic Systems M.S. program require students to do a substantial project. The course requirements for each track are designed to prepare a student to undertake such a project. The nature of the project depends on the student's focus, but it should be academic in nature (contributing to generalizable knowledge) and it should relate to the subject matter of symbolic systems more or equally appropriately as it does to other master's degree programs at Stanford. In all cases, a written thesis or paper describing the project is required. 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. Each track of the Symbolic Systems M.S. program has its own core requirements, as well as unit requirements from a set of elective courses. The tracks and their requirements are as follows.

The Human-Computer Interaction (HCI) Track—The HCI Core: a course in Computer Science numbered 141-179 (excluding 147), or CS 241-279 (excluding 247), or CS 295, Software Engineering; and CS 147, Introduction to Human-Computer Interaction Design; and CS 247, Human-Computer Interaction: Interaction Design Studio; and CS 376, Research Topics in Human-Computer Interaction or COMM 269, Computers and Interfaces.

For HCI electives, at least 9 additional units of HCI courses, chosen in consultation with the student's adviser. The following are examples of themes around which an elective program might be built: animation, business systems, computer-aided design, computer graphics, data interfaces, decision systems, design for disabilities, design principles, dialogue systems, digital art, digital media, education technology, game design, history of computers, information retrieval, intelligent interfaces, interaction design, internet design, medical informatics, multimedia design, object-oriented design, philosophy of computation, social aspects of computing, usability analysis, virtual reality, and workplace computing.

The Natural Language Technology (NLT) Track—For the NLT core, in addition to the courses below, students must complete LINGUIST 284/CS 224N, Natural Language Processing, which can be used as the empirical methods course for Requirement B above.

  1. An in-depth theory of English grammar course such as LINGUIST 221A, Foundations of English Grammar
  2. A graduate-level semantics course (if not already taken as part of Requirement A) such as LINGUIST 232A, Lexical Semantics, or 230B, Semantics and Pragmatics
  3. A two-course sequence in Computational Linguistics:
    1. LINGUIST 280. From Languages to Information, and
    2. LINGUIST 282. Computational Theories of Syntax

For NLT electives, at least 9 additional units of natural language technology courses, chosen in consultation with the student's adviser.

The Individually Designed Option—Students wishing to design their own M.S. curriculum in Symbolic Systems must present a project plan as part of their application. This plan must be endorsed by the student's adviser prior to admission to the Symbolic Systems M.S. program. The application must also specify at least 20 units of course work that the student plans tof take in support of the project.

Students are admitted under this option only if they present well-developed plans whose interdisciplinary character makes them inappropriate for any departmental master's program, but appropriate for Symbolic Systems.


The following is a list of cognate courses that may be applied to the M.S. in Symbolic Systems. See respective department listings for course descriptions and General Education Requirements (GER) information.

BIO 20. Introduction to Brain and Behavior (Same as HUMBIO 21)

BIO 150/250. Human Behavioral Biology (Same as HUMBIO 160)

BIO 153. Cellular Neuroscience: Cell Signaling and Behavior

CME 106. Introduction to Probability and Statistics for Engineers (Same as ENGR 155C)

COMM 106/206. Communication Research Methods

COMM 169/269. Computers and Interfaces

COMM 172/272. Media Psychology

CS 103. Mathematical Foundations of Computing

CS 103A. Discrete Mathematics for Computer Science

CS 103B. Discrete Structures

CS 103X. Discrete Structures (Accelerated)

CS 106A. Programming Methodology (Same as ENGR 70A)

CS 106B. Programming Abstractions (Same as ENGR 70B)

CS 106X. Programming Abstractions (Accelerated) (Same as ENGR 70X)

CS 107. Computer Organization and Systems

CS 108. Object-Oriented Systems Design

CS 109. Introduction to Probability for Computer Scientists

CS 110. Principles of Computer Systems

CS 121. Introduction to Artificial Intelligence

CS 124. From Languages to Information (Same as LINGUIST 180/280)

CS 142. Web Applications

CS 147. Introduction to Human-Computer Interaction Design

CS 148. Introductory Computer Graphics and Imaging

CS 154. Introduction to Automata and Complexity Theory

CS 157. Logic and Automated Reasoning

CS 161. Design and Analysis of Algorithms

CS 170. Composition, Coding, and Performance with SLOrc (Same as MUSIC 128)

CS 181. Computers, Ethics, and Public Policy

CS 193D. Professional Software Development with C++

CS 193S. Scalable Web 2.0 Programming

CS 204. Computational Law

CS 205A. Mathematical Methods for Robotics, Vision, and Graphics

CS 207. The Economics of Software

CS 208. The Canon of Computer Science

CS 221. Artificial Intelligence: Principles and Techniques

CS 222. Rational Agency and Intelligent Interaction (Same as PHIL 358)

CS 223A. Introduction to Robotics

CS 223B. Introduction to Computer Vision

CS 224M. Multi-Agent Systems

CS 224N. Natural Language Processing (Same as LINGUIST 284)

CS 224S. Speech Recognition and Synthesis (Same as LINGUIST 285)

CS 224U. Natural Language Understanding (Same as LINGUIST 188/288)

CS 227. Reasoning Methods in Artificial Intelligence

CS 228. Structured Probabilistic Models: Principles and Techniques

CS 228T. Structured Probabilistic Models: Theoretical Foundations

CS 229. Machine Learning

CS 247. Human-Computer Interaction Design Studio

CS 249A. Object-Oriented Programming from a Modeling and Simulation Perspective

CS 276. Information Retrieval and Web Search (Same as LINGUIST 286)

CS 303. Designing Computer Science Experiments

CS 376. Research Topics in Human-Computer Interaction

CS 377. Topic in Human-Computer Interaction

CS 377L. Learning in a Networked World (Same as EDUC 298)

CS 378. Phenomenological Foundations of Cognition, Language, and Computation

CS 547. Human-Computer Interaction Seminar

ECON 51. Economic Analysis II

ECON 137. Information and Incentives

ECON 160. Game Theory and Economic Applications

EDUC 218. Topics in Cognition and Learning: Play

EDUC 298. Learning in a Networked World (Same as CS 377L)

EE 178. Probabilistic Systems Analysis

EE 376A. Information Theory

ENGR 62. Introduction to Optimization (Same as MS&E 111)

ENGR 155C. Introduction to Probability and Statistics for Engineers (Same as CME 106)

ETHICSOC 20. Introduction to Moral Philosophy (Same as PHIL 20)

ETHICSOC 30. Introduction to Political Philosophy (Same as PHIL 30, PUBLPOL 103A)

HPS 60. Introduction to Philosophy of Science (Same as PHIL 60)

HUMBIO 21. Introduction to Brain and Behavior (Same as BIO 20)

HUMBIO 145. Birds to Words: Cognition, Communication, and Language (Same as PSYCH 137/239A)

HUMBIO 160. Human Behavioral Biology (Same as BIO 15/250)

LINGUIST 1. Introduction to Linguistics

LINGUIST 105/205A. Phonetics

LINGUIST 110. Introduction to Phonetics and Phonology

LINGUIST 120. Introduction to Syntax

LINGUIST 124A/224A. Introduction to Formal Universal Grammar

LINGUIST 130A. Introduction to Linguistic Meaning

LINGUIST 130B. Introduction to Lexical Semantics

LINGUIST 140/240. Language Acquisition I

LINGUIST 180/280. From Languages to Information (Same as CS 124)

LINGUIST 181/281. Grammar Engineering

LINGUIST 182/282. Computational Theories of Syntax

LINGUIST 188/288. Natural Language Understanding (Same as CS 224U)

LINGUIST 210A. Phonology

LINGUIST 210B. Advanced Phonology

LINGUIST 221A. Foundations of English Grammar

LINGUIST 221B. Studies in Universal Grammar

LINGUIST 222A. Foundations of Syntactic Theory I

LINGUIST 230A. Introduction to Semantics and Pragmatics

LINGUIST 230B. Semantics and Pragmatics

LINGUIST 232A. Lexical Semantics

LINGUIST 235. Semantic Fieldwork

LINGUIST 241. Language Acquisition II

LINGUIST 247. Seminar in Psycholinguistics (Same as PSYCH 227)

LINGUIST 278. Programming for Linguists

LINGUIST 284. Natural Language Processing (Same as CS 224N)

LINGUIST 285. Speech Recognition and Synthesis (Same as CS 224S)

LINGUIST 286. Information Retrieval and Web Search (Same as CS 276)

LINGUIST 289. Quantitative, Probabilistic, and Optimization-Based Explanation in Linguistics

MATH 113. Linear Algebra and Matrix Theory

MATH 151. Introduction to Probability Theory

MATH 162. Philosophy of Mathematics (Same as PHIL 162)

ME 115B. Product Design Methods

MS&E 120. Probabilistic Analysis

MS&E 121. Introduction to Stochastic Modeling

MS&E 201. Dynamic Systems

MS&E 430. Tools for Experience Design

MUSIC 151. Psychophysics and Cognitive Psychology for Musicians

MUSIC 128. Composition, Coding, and Performance with SLOrc (Same as CS 170)

MUSIC 220A. Fundamentals of Computer-Generated Sound

MUSIC 220B. Compositional Algorithms, Psychoacoustics, and Spatial Processing

MUSIC 250A. HCI Theory and Practice

MUSIC 251. Music, the Brain, and Human Behavior

MUSIC 253. Musical Information: An Introduction

MUSIC 254. Applications of Musical Information: Query, Analysis, and Style Simulation

NBIO 206. The Nervous System

NBIO 218. Neural Basis of Behavior

PHIL 9N. Philosophical Classics of the 20th Century

PHIL 10. God, Self, and World: An Introduction to Philosophy

PHIL 80. Mind, Matter, and Meaning

PHIL 102. Modern Philosophy, Descartes to Kant

PHIL 143/243. Quine

PHIL 150. Basic Concepts in Mathematical Logic

PHIL 151. First-Order Logic

PHIL 152. Computability and Logic

PHIL 154. Modal Logic

PHIL 155. General Interest Topics in Mathematical Logic

PHIL 157. Topics in Philosophy of Logic

PHIL 164. Central Topics in the Philosophy of Science: Theory and Evidence

PHIL 166. Probability: Ten Great Ideas About Chance

PHIL 167B. Philosophy, Biology, and Behavior

PHIL 180A/280A. Realism, Anti-Realism, Irrealism, Quasi-Realism

PHIL 181. Philosophy of Language

PHIL 184. Theory of Knowledge

PHIL 184B. Philosophy of the Body

PHIL 184P. Probability and Epistemology

PHIL 186. Philosophy of Mind

PHIL 187. Philosophy of Action

PHIL 188. Personal Identity

PHIL 189/289. Examples of Free Will

PHIL 194C. Time and Free Will

PHIL 194P. Naming and Necessity

PHIL 194R. Epistemic Paradoxes

PHIL 279. Collectivities

PHIL 350A. Model Theory

PHIL 351A. Recursion Theory

PHIL 354. Topics in Logic

PHIL 366. Evolution and Communication

PHIL 382A. Pragmatics and Reference

PHIL 387. Practical Rationality

PHIL 387C. Consistency and Coherence

PSYCH 55. Introduction to Cognition and the Brain

PSYCH 104. Uniquely Human

PSYCH 122S. Introduction to Cognitive and Comparative Neuroscience

PSYCH 131/262. Language and Thought

PSYCH 133. Human Cognitive Abilities

PSYCH 134. Seminar on Language and Deception

PSYCH 141. Cognitive Development

PSYCH 143. Developmental Anomalies

PSYCH 154. Judgement and Decision-Making

PSYCH 159. Psychology of Attitude Change and Social Influence

PSYCH 202. Cognitive Neuroscience

PSYCH 204A. Human Neuroimaging Methods

PSYCH 209/209A. The Neural Basis of Cognition: A Parallel Distributed Processing Approach

PSYCH 209B. Applications of Parallel Distributed Processing Models to Cognition and Cognitive Neuroscience

PSYCH 226. Models and Mechanisms of Memory

PSYCH 227. Seminar in Psycholinguistics (Same as LINGUIST 247)

PSYCH 232. Brain and Decision Making

PSYCH 246. Cognitive and Neuroscience Friday Seminar

PSYCH 250. High-level Vision

PSYCH 251. Affective Neuroscience

PSYCH 252. Statistical Methods for Behavioral and Social Sciences

PSYCH 253. Statistical Theory, Models, and Methodology

PSYCH 272. Special Topics in Psycholinguistics

SOC 126/226. Introduction to Social Networks

STATS 110. Statistical Methods in Engineering and the Physical Sciences

STATS 116. Theory of Probability

STATS 191. Introduction to Applied Statistics

STATS 200. Introduction to Statistical Inference

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