Concentrations - G1

Old Symbolic Systems Undergraduate Concentrations -- available only to students who declared SSP through Autumn Quarter 2001-2002

In addition to the core, SSP majors choose an area of concentration comprising five courses. There is far greater flexibility in the make-up of a concentration than there is in the core requirement, which is quite strictly upheld. Our primary concern is that students identify an area of particular interest within the general sphere of SSP's subject matter, and design a coherent set of courses around this. They can do so in consultation with the student advisors, their Faculty Advisors, the Director, or the Coordinator. To guide students in their selection, SSP faculty members have drawn up sample concentrations, which students may elect to follow precisely or adopt with limited changes. Alternatively, students may design their own concentrations from scratch.

The suggested concentrations are: Applied Logic, Artificial Intelligence, Cognition, Computer Music, Education and Learning, Human-Computer Interaction, Natural Language, Neural Systems, Philosophical Foundations, and Rationality. Frequently, courses listed in the concentrations below serve as examples only, so that unlisted courses, covering similar material, could be substituted for them. Students should discuss course selection with their advisors.

Note: no course may count towards both core and concentration requirements.

  • Applied Logic
    1.
    One course emphasizing a computational approach to logic. For example: Logic and Automated Reasoning (CS 157), Automated Deduction and its Applications (CS 257), Reasoning Methods in AI (CS 227)
    2.
    One course in set theory. For example: Set Theory (Math 161), Set Theory (Math 292A)
    3.
    One additional course in formal semantics. For example: Intensional Logic (Phil 169), Semantics and Pragmatics (Ling 230A)
    4.
    One additional course in computability theory. For example: Introduction to Automata and Complexity Theory (CS 154) (if not taken for core), Computability and Logic (Phil 160B) (if not taken for core), Topics in Complexity Theory (CS 351)
    5.
    One additional advanced course in logic. For example: Automated Deduction and its Applications (CS 257), Model Theory (Math 290A), Recursion Theory (Math 291A), Proof Theory (Math 293A)
  • Artificial Intelligence

    For this concentration, students must take CS 221 to satisfy the core AI requirement; they should also take a course in probability theory (Statistics 116 or EESOR 120) for the math requirement. In addition, they must complete a total of five courses from the following list, including at least three of the classes marked in boldface:

    1.
    Knowledge representation and reasoning: Logic and Automated Reasoning (CS 157); Knowledge Representation (CS 222); Reasoning Methods in AI (CS 227); Reasoning under Uncertainty (CS 228); Nonmonotonic Common Sense Reasoning (CS 323).
    2.
    Natural language processing: Natural-Language Processing (CS 224N) or Introduction to Computational Linguistics (Ling 138) (but not both).
    3.
    Learning: Machine Learning (CS 229); Genetic Algorithms and Genetic Programming (CS 426); Learning and Inference in Humans and Machines (Psych 224).
    4.
    Robotics and vision: Introduction to Robotics (CS 223A); Introduction to Computer Vision (CS 223B); Experimental Robotics (CS 225A); Robot Programming Laboratory (CS 225B); Motion Planning (CS 326A); Topics in Computer Vision (CS 328).
    5.
    Additional topics: Multi-agent Systems (CS 224M); Introduction to Medical Informatics: Fundamental Methods (CS 270A/MIS 210A); Decision-Making Methods for Biomedicine (CS 271/MIS 211); Phenomenological Foundations of Cognition, Language, and Computation (CS 378); Representations and Algorithms for Computational Molecular Biology (CS 274).
  • Cognition

    For this concentration, students should use Philosophy of Mind (Phil 186) towards fulfillment of their core requirement. In addition, they must complete a total of five courses drawn from at least four of the following six areas.

    1.
    Perception. For example: Introduction to Perception (Psych 30), Applied Vision and Image Systems (Psych 221)
    2.
    Psycholinguistics. For example: Language and Thought (Psych 131), Language Processing (Psych 132), Language Acquisition I and/or II (Psych 240, 241)
    3.
    Cognitive Development and Language Acquisition. For example: Cognitive Development (Psych 141), Language Acquisition I and/or II (Psych 240, 241)
    4.
    Computational Approaches to Cognition. For example: Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Psych 265)
    5.
    Cognition and Thought. For example: Foundations of Cognitive Science (Psych 200), Psychology of Problem Solving and Reasoning (Edu 295), Research Methods in Cognitive Psychology (Psych 112)
    6.
    Neuroscience. For example: Introduction to Human Neuropsychology (Psych 50), The Nervous System (Neurobiology 200)

     

  • Computer Music
    1.
    Fundamentals of Computer-Generated Sound (Music 220A)
    2.
    Compositional Algorithms, Psychoacoustics and Spatial Processing (Music 220B)
    3.
    Three courses from the following list, of which at least 2 must be from Group A:

     

    Group A - Courses in Computer Music

    • Introduction to Music Composition and Programming Using MIDI Based Systems (Music 120)
    • Psychophysics and Cognitive Pyschology for Musicians (Music 151)
    • History of Electroaucustic Music (Music 154)
    • Seminar: Computer-Music Research (Music 220C)
    • Seminar: Topics in Computer Music (Music 252)
    • Musical Information: An Introduction (Music 253)
    • Seminar: Musical Representation and Computer Analysis (Music 254)

     

    Group B - Related Courses

    • Programming Paradigms (Computer Science 107)
    • Introduction to Human-Computer Interaction Design (Computer Science 147)
    • C++ and Object-Orientated Programming (Computer Science 193D)
    • Programming in LISP (Computer Science 193L)
    • Topics in Human-Computer Interaction (Computer Science 377)
    • Phonetics (Linguistics 105)
    • Introduction to Phonetics and Phonology (Linguistics 110)
    • Introduction to Perception (Psych 30)

     

  • Human-Computer Interaction
    1.
    Introduction to HCI Design (CS 147)
    2.
    A project-based course involving a group design or analysis project in human computer interaction. This can be satisfed by the project courses on Human Computer Interaction, CS247a, CS247b, CS447 or other courses that will be approved on an individual basis.
    3.
    Three more courses, one from each of the following groups:
    • Social. For example: Communication, Technology, and Society (Communication 169); Work, Technology, and Society (IEEM 170); Human Values in Design (Mechanical Engineering 115A); Science, Technology, and Contemporary Society (STS 101); Computers, Ethics, and Social Responsibility (CS 201).
    • Cognitive. For example: Introduction to Perception (Psych 30); Language and Thought (Psych 131); Applied Vision and Image Systems (Psych 221); Phenomenological Foundations of Cognition, Language, and Computation (CS 378)
    • Technical. For example: Object-Oriented Systems Design (CS 108); C++ and Object-Oriented User Programming (CS 193D); Internet Technologies (CS 191I); Programming in Java (CS 192J); Object-Oriented Programming from a Modeling and Simulation Perspective (CS 249).

       

    Each quarter special topics are given under the number of CS 377: Topics in Human-Computer Interaction. Topics vary, and can be taken to fulfill the requirement above to which the specific topic is appropriate.

  • Learning

    For this concentration, students must take CS 221 to satisfy the core AI requirement; they should also take a course in probability theory (Statistics 116 or Management Science and Engineering 120) for the statistics requirement. In addition, they must complete a total of five courses from the following list, in at least three of the following four areas:

    1.
    Statistical Learning: Machine Learning (CS 229); Probabilistic Models for Artificial Intelligence (CS 228); Modern Applied Statistics: Learning and Data Mining (Stat 315A,B,C); Information Theory (EE 376A); Neuro-Dynamic Programming and Reinforcement Learning (Management Science and Engineering 339).
    2.
    Psychology of Learning: Culture and Learning (EDU 287); Cognitive Development (Psych 141); Conceptual Organization and Development (Psych 144); Memory and Learning (Psych 210); Learning and Inference in Humans and Machines (Psych 224); Learning and Cognition in Activity (EDU 295); Advanced Seminar in Learning Design and Technology (EDU 333A,B).
    3.
    Neuroscience: Introduction to Human Neuropsychology (Psych 50); Neuroscience (Psych 202); Memory Systems (Psych 262).
    4.
    Language and Learning: Language acquisition (Ling 240, 241); Natural Language Processing (CS 224N); Selected Topics in Human Learning (Psych 264).
  • Natural Language

    Five of the following courses, at least one from each of areas 1, 2, and 3.

    1.
    Computation.
    • Introduction to Computational Linguistics (Ling 138)
    • Natural Language Processing (Ling 237)
    • Phenomenological Foundations of Cognition, Language, and Computation (CS 378)
    2.
    Semantics.
    • Introduction to Semantics and Pragmatics (Ling 230A)
    • Philosophy of Language (Phil 181)
    • Any seminar or topics course in semantics.
    3.
    Syntax.
    • Intermediate Syntax (Ling 121)
    • Cross-Linguistic Syntax I (Ling 220A)
    • Cross-Linguistic Syntax II (Ling 220B)
    • Introduction to Head-Driven Phrase Structure Grammar
      (Ling 221A)
    • Introduction to Formal Universal Grammar (Ling 224A)
    • Any upper division or topics course in syntax.
    4.
    Other.
    • Any advanced undergraduate or graduate linguistics course.
    • Introduction to Phonetics and Phonology (Ling 110)
    • Language Acquisition I (Ling 240)
    • Introduction to Automata and Complexity Theory
      (CS 154)
    • Language and Thought (Psych 131)
    • Language Processing (Psych 132)

     

  • Neural Systems

    This concentration combines biological, cognitive, and computational approaches to neuroscience and neural modeling. Students should choose a total of five courses from at least three of the following areas, with at least one course coming from the basic neuroscience section. It is recommended that students who are interested in computational approaches satisfy their core mathematics requirement by taking a course in linear algebra (such as Math 103), probability theory (such as Statistics 116), or differential equations (such as Math 130); they should also take CS 221 to satisfy the core AI requirement. Students who are interested primarily in experimental approaches should take a course in inferential statistics (such as Stat 190).

    1.
    Basic neuroscience. For example: Cellular Neuroscience: Cell Signaling and Behavior (Biology 153), Human Behavioral Biology (Biology 150), The Nervous System (Neurobiology 200)%  latex2html id marker 1726 \setcounter{footnote}{1}\fnsymbol{footnote}, Introduction to Brain and Behavior (Psych 20)
    2.
    Advanced neuroscience approaches. For example: Behavioral Neuroscience (Psych 206)
    3.
    Computational approaches. For example: Foundations of Cognitions (Psych 205), Learning and Inference in Humans and Machines (Psych 224), Machine Learning (CS 229)
    4.
    Biological and computational approaches to vision. For example: Introduction to Perception (Psych 30), Applied Vision and Image Systems (Psych 221), Foundations of Vision (Psych 203)
    5.
    Cognitive science/cognitive neuroscience approaches. For example: Introduction to Human Neuropsychology (Psych 50), Graduate Seminar in Cognitive Neuroscience (Psych 123), Consciousness (Phil 194C), Memory Systems (Psych 262)

     

  • Philosophical Foundations
    1.
    Philosophy of Language (Phil 181), Philosophy of Mind (Phil 186), or Philosophical Applications of Cognitive Science (Phil 189).
    2.
    A course in the history of philosophy, for example:
    Modern Philosophy, Descartes to Kant (Phil 102)
    3.
    A course in value theory, for example:
    Computers, Ethics, and Social Responsibility (CS 201)
    4.
    Two of the following:
    • A logic course from the Math 290 (=Phil 290) series or Intensional Logic (Phil 169)
    • A course in the philosophy of science, for example: Central topics in the Philosophy of Science (Phil 164), Philosophy of Biology (Phil 167)
    • A course in AI or foundations of AI, for example:
      Knowledge Representation (CS 222); Phenomenological Foundations of Cognition, Language, and Computation (CS 378)
    • A course in metaphysics or epistemology, for example:
      Theory of Knowledge (Phil 184); Contextualism/Skepticism (Phil 185), The Structure of Cognition: Introduction to Husserl's Phenomenology (Phil 131)
  • Rationality

    For this concentration, students should first take either Probability (Stat 116 or EESOR 120) to fulfill their core mathematics requirement. Taking CS 221 rather than CS 121 is strongly recommended. The remaining courses can be taken in any order. (The concentration can be completed without the need to take courses outside the core to fulfill prerequisites, provided that one of the two above statistics courses is taken. However, some of the courses listed below, those with an asterisk, may have additional prerequisites that are not covered by the Symbolic Systems core, or may require special permission for undergraduates. Students who choose to take these courses are responsible for meeting the requirements for entry.) Requirements include five courses, with at least one from each of the following areas, and the fifth to come from area 3 or area 4:

    1.
    Psychology of rational behavior: Introduction to Social Psychology (Psych 70); Applications of Social Psychology (Psych 156); Theoretical Approaches in Social Psychology (Psych 157); Learning and Inference in Humans and Machines (Psych 224); Seeing Reason: An Interdisciplinary approach (Phil 157).
    2.
    Ethics and Society Ethics of Social Decisions (Phil 77); Medical Ethics (Phil 78); Is Morality too Demanding (Phil 172); Morality and Law (Phil 157); The Ethical Analyst (EESOR 254); Economics of Public Policy (Econ 150); Economics of Legal Rules and Institutions (Econ 154); Welfare Economics (Econ 280); Computers, Ethics, and Social Responsibility (CS 201); Economics of Health and Medical Care (Econ 156); Risk Analysis: Theoretical, Legal and Economic Perpsectives (EESOR 245).
    3.
    Normative Foundations: Economic Analysis I (Econ 50); Economic Analysis II (Econ 51); Economics of Uncertainty (Econ 281); Introduction to Decision Analysis (EESOR 152); Decision Analysis I (EESOR 252); Decision Analysis II (EESOR 352); Game Theory and Economic Applications (Econ 160); Philosophy of action (Phil 187); Information Theory (EE 376A).
    4.
    Computational Rationality Reasoning Under Uncertainty (CS 228); Multi-Agent Systems (CS 224M); Intro to Optimization (ENGR 62); Linear and Nonlinear Optimization (EESOR 211); Influence Diagrams and Probabilistic Networks (EESOR 355); Decision-Making Methods for Biomedicine (CS 271 / MIS 211).
  • Individually Designed Concentration

    Five courses in a coherent subject area. Course selection is to be made in consultation with the student's advisor and is subject to approval by the advisor and the Director. Some recent examples of individually designed concentrations: Robotics, Vision and Visual Processing, Computer Graphics, Computer Speech Technology, Emergence, and Computer Assisted Language Learning.

     


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