Computational Cognitive Science Track

This is a specialized track within the PhD program in Cognitive Science.

Admissions Prerequisites

Students entering this track should already have programming and math skills that would allow them to take the basic computation courses (e.g. experience with python or MATLAB, linear algebra, calculus, etc.). Visit the Cognitive Science PhD program admissions web page.

Goals

Students in this track will obtain a depth of focus in computational coursework, not achieved in the PhD in Cognitive Science general requirements. Accordingly, some of the breadth coursework with basic computational courses have been replaced, while aiming to retain the spirit of the breadth requirement.

Coursework Requirements

[Degree Checklist for CCS Track]

Courses may not be double-counted. Each course may only be used to satisfy a single degree requirement, even if it may qualify for more than one requirement.

  • Breadth (3-4 courses): These courses should be offered by the Department of Cognitive Science and they must collectively develop sophistication in both (1) theoretical approaches to cognitive science (e.g. theory in linguistics/psychology) and (2) (human) experimental approaches to cognitive science.
    • At least one course in language area
    • At least one course in vision area
  • Basic computation (3 courses): Examples of courses that apply include Machine learning, Foundations of Neural Network Theory, Bayesian Inference, Mathematical Models of Language, and Data science.
  • Integration (2 courses): Foundations of Cognitive Science and Professional Seminar in Cognitive Science, or Departmental Seminar, or other dept-wide seminar explicitly offered in lieu of these.
  • Responsible Conduct in Research
  • Depth (6-8 courses): Selected in conjunction with adviser(s) to achieve depth and expertise in specific areas of computational cognitive science. Adviser may consult with Director of Graduate Studies. Examples of courses that apply include Natural Language Processing, Probabilistic Models of the Visual Cortex, Events Semantics in Theory and Practice, and Vision as Bayesian Inference.