# 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
Cross-listed with:
COGS-6210 Cognitive Modeling I CSCI-4961 Topics in CSCI PSYC-4510 Cognitive Modeling
Prereqs:
CSCI-2300 Introduction to Algorithms

# Past Term Data
Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
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| 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
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