Course List For Fall 2009

This is not a complete list of courses. For the most current course information and availability, please consult the timetable.

Computational Cognitive Science (CS838)

Instructor: Michael Coen. Department of Biostatistics & Medical Informatics

This introductory class addresses two basic questions: 1. How can understanding intelligence in animals lead to the development of more sophisticated computational systems? 2. How can biologically-inspired machine learning lead to new theories of learning and cognition in animals? We'll examine how these two questions are surprisingly interrelated, using formal methods from cognitive science, artificial intelligence, machine learning, computational biology, and cognitive neuroscience. Along the way, we'll examine applications from a variety of domains, including artificial life, concept and language acquisition, programmatic trading on Wall Street, and automatically segmenting brain regions based on function. Prerequisites: Because this class is interdisciplinary, students from all backgrounds are welcome. Some mathematical background would be helpful, particularly linear algebra and calculus, but the course will be essentially self-contained. Also helpful would be previous exposure to basic concepts in computer science and programming in a high-level language, such as Matlab or Python. (E.g., CS302 would be more than sufficient.) If you have any questions about prereqs, please contact me; we'll make every effort to accommodate students from a variety of disciplines.

Psy 733 Perceptual & Cognitive Sciences: Speech Perception

Instructor: Keith Kluender. Department of Psychology

This 8-week course provides an introduction to current understanding of speech perception. Speech perception research is situated within general principles of perception including analogies with contemporary approaches to high-level vision. General issues pertaining to cognitive psychology and psycholinguistics include: ways that basic perceptual capacities shape perception and production of speech; perceptual development; perceptual categorization; and applications to learning a second language among other topics.

EdPsych 920: Development of Inductive Inference

Instructor: Chuck Kalish. Department of Educational Psychology

This course willprovide a basic introduction to the study of inductive inference. Topics will include categorization, causal inference, and probabilistic reasoning. A primary focus will be development: How do children reason, and how do adults come to have the reasoning patterns they do? We will consider inductive inference in key educational domains: Science, Mathematics, and Reading, and explore classic claims that people are irrational (e.g., Kahneman & Tversky; Wason). The class will be organized as a seminar, with emphasis on in-class discussion and presentations. I expect to tailor specific topics to the interests of class participants.

EdPsych 795: Introduction to Learning Sciences.

Instructor: Mitchell Nathan. Department of Educational Psychology

The course serves as a requirement for students in the PhD program in Ed Psych in the Learning Sciences program area, and therefore covers material that is necessary for that training. It is the first of a two-course sequence (795 & 796). The fall course (795) includes a review of the historical work and scientific study of cognition from a variety of theoretical perspectives (experimental psychology, AI and computer science, philosophy of mind, and socio-cultural perspectives. There are several student presentations as a cumulative research project. 795 satisfies the cognitive and affective requirements for several programs on campus, and you should check that this is the case for your program. The second course (796), offered in spring, deals more directly with issues of classroom learning and instruction, learning needs for a diverse population (some discussion of special needs learners, those from minority populations), identity, educational technology, and classroom based programs of instruction that have been developed based on research by those in cognitive science and learning science. It also explores emerging areas such as cognitive neuroscience, gesture studies and embodied cognition. The project work that is begun in 795 is more fully developed and formally presented by the end of 796. You must take 795 before enrolling in 796. Students are ENCOURAGED to take both courses in sequence.

Psych 711: Introduction to Parallel Distributed Processing

Instructor: Tim Rogers. Department of Psychology

The goal of the course is to introduce the basic principles of parallel distributed processing (also known as connectionist or neural network modeling) and to illustrate how these principles provide insight into human perceptual, linguistic, and cognitive behavior. In addition, the course will cover some issues in neural and cognitive development, cognitive impairments due to brain damage, and some basic computational issues. The course also introduces the general practice of studying cognition through computational modeling and analysis. There will be computer simulation exercises in addition to readings. Homework assignments will generally require you to report the results of simulations you have carried out, to analyze these results, and to think critically about some issues raised in the readings. There will also be a final project that will typically involve simulation modeling

CS 540: Introduction to Artificial Intelligence

2 sections. Instructors: Charles Dyer and Jerry Zhu