Course Syllabus

Course description:  Provides a computational toolbox for research in cognitive science as well as an introduction to computational models of cognition, summarizing traditional approaches and providing experience with state-of-the-art methods. Covers pattern recognition and neural networks as well as Bayesian probabilistic models and other machine learning approaches. Illustrates how they have been applied in several key areas in cognitive science, including visual perception, object and face recognition, learning and memory as well as inductive and causal reasoning. Focuses on modeling simple laboratory tasks from cognitive psychology. Connections to contemporary research will be emphasized highlighting how computational models may motivate the development of new hypotheses for experiment design in cognitive psychology.

Course objectives: Computational modeling is one of the central methods in brain and cognitive science research, and recent developments in computational neuroscience, artificial intelligence, machine learning, and statistics have provided a wealth of new tools for the analysis of experimental data and for developing computational accounts of human cognition. The objective of this course is to provide students a toolkit (MATLAB programming, mathematical techniques, computational methods) for modeling human cognition. At the end of this course, students will at least be able to identify which type of model would be best to use to fit a given experimental problem, and evaluate the quality of such models. Advanced students will also be able to independently generate such models.

A life-long learning outcome of this course is for students to gain computational thinking skills. Computational thinking is a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science. Computational thinking is a fundamental skill for everyone, not just for computer scientists. Computational thinking has become an important part of the way people think and understand the world, and it has become a fundamental skill to succeed in today's world.

Who should take this course: The course is designed for students in psychology, cognitive science, cognitive neuroscience, and neuroscience interested in developing computational skills broadly defined. The inherently interdisciplinary nature of the subject is reflected in the course, which brings together issues relating to the disciplines of cognitive psychology, neuroscience, computer science and machine learning.  

Prerequisites: Basic linear algebra and probability theory. This course is intended for students with little or no programming experience. 

Format: There will be two 80 minute lectures per week. There will also be weekly tutorial sections, covering MATLAB programming and math concepts in more detail. These tutorials will be optional, but highly recommended for students with limited programming experience.

Readings: There is no textbook for the class. Readings will be limited, and may consist of a handful of journal articles or book chapters. Optional supplementary readings may be attached to computational assignments for students seeking additional background information. Please check Course Schedule below and Modules for occasional readings.

Office Hours: 
Dr. Serre
- Wednesday & Thursday 1-2pm, Metcalf 012
(undergraduate TA) - Thursday 7-9pm, Metcalf 107
(undergraduate TA) - Sunday 12-2pm, CIT 269

Desktop computers will be accessible during all TA office hours. 

= 90-100
B = 80-90
C = 70-80
NC = <70

Breakdown of Assessment:
Final project:
Up to 10% extra credit

Students can earn 1 point for attending and actively participating in weekly tutorial sections. Students can earn up to 2 points for completing an extra credit question at the end of each weekly assignment. A maximum of 2 extra credit points total can be earned per week.

Students who initially feel less comfortable with the computational aspects of the course are encouraged to attend weekly sessions and attempt extra credit questions.

Students with stronger computational backgrounds may receive maximum points by earning full credit on the extra credit question, and do not need to attend the optional tutorial if they feel comfortable with the introductory material.

Weekly assignments will be made available in Assignments. All assignments must be uploaded to Canvas. For further instructions, please check the Assignment Guidelines here.

Course Summary:

Date Details