This course focuses on: (a) statistical tools that are useful for revealing structure in experimental data; and (b) representation and learning in statistical systems and the implications of these systems for the study of cognitive processes. Examples of the applications of computational methods from the cognitive neuroscience literature are examined throughout the course. Topics covered include: principal component analysis, multi-dimensional scaling, mixture models, hierarchical clustering, artificial neural networks, regression, classification, hidden Markov models, Kalman filters, Hebbian learning, competitive learning, maximum likelihood estimation, and Bayesian estimation.
Prerequisites include knowledge of calculus. Knowledge of linear algebra and probability theory will also be helpful (though prior knowledge of these areas is not strictly required). In addition, homeworks require students to write computer programs (preferably in Matlab).
Readings
Mathematical notes written by the instructor. Please bring these notes to class.
Journal articles and book chapters made available throughout the semester.
Requirements
Class attendance and participation are mandatory.
Readings are mandatory.
There will be four homework assignments. Each assignment will include both written problems to solve and computer
models to implement (see below).
Students will give formal presentations of articles and chapters that illustrate the application of computational
methods to problems in cognitive science. Each presentation should be limited to 20-25 minutes. Students should use
PowerPoint during their presentations.
Students must do course projects. Projects may be on any topic subject to approval by the instructor. At the end of
the semester, students will present their projects to the class (30-35 minute presentations), and also turn in a 10 page
report on their projects.
All coursework must be done individually. The exception is the course projects for which two
students may want to work together (on a project twice as large as a project completed by a single
individual). In addition, all coursework must be completed during the semester (i.e., students will
not be permitted to take incompletes).
Computer simulations: Homeworks will require students to simulate statistical models. Everyone must write their own simulations,
preferably using Matlab. If you do not know Matlab, then your first homework assignment is to complete a Matlab tutorial.