Cognitive Modeling

COGS-4210

Recent advances in Cognitive Science, Computer Science and Mathematics, have resulted in the ability to develop computer programs that implement Probabilistic Cognitive Models (PCMs). The cognitive models that this course covers are based on approximate Bayesian Inference implemented by Markov Chain Monte Carlo and Variational techniques that have made this approach tractable. The objective of this course is to enable the student to develop models of cognition in a Bayesian framework.

4 credits

Past Term Data

Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
Spring Summer Fall
(Session 1) (Session 2)
2023
2022
2021
Cognitive Modeling (4c)
  • Michael J. Schoelles
Seats Taken: 15/24
2020
Cognitive Modeling (4c)
  • Michael J. Schoelles
Seats Taken: 10/24
2019
Cognitive Modeling (4c)
  • Michael J. Schoelles
Seats Taken: 11/24
2018
Cognitive Modeling (4c)
  • Michael J. Schoelles
Seats Taken: 6/7
2017
Cognitive Modeling (4c)
  • Michael J. Schoelles
Seats Taken: 6/7
2016
Cognitive Modeling I (4c)
  • Michael J. Schoelles
Seats Taken: 5/7
2015
Cognitive Modeling I (4c)
  • Michael J. Schoelles
Seats Taken: 4/5
2014
2013
Cognitive Modeling I (4c)
  • Michael J. Schoelles
Seats Taken: 5/6
2012
Cognitive Modeling I (4c)
  • Ron Sun
Seats Taken: 3/5
2011
Cognitive Modeling I (4c)
  • Michael J. Schoelles
Seats Taken: 2/2
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998