# Human & Machine Learning

## COGS-4230

This seminar explores the growing convergence between computational theories of human and machine learning. The course will focus on major theoretical frameworks including deep learning, Bayesian inference, information theory, and reinforcement learning, utilizing journal articles from both machine learning and cognitive science literatures. These topics will be critically evaluated from the perspective of how computational theories can be instantiated in cognitive systems. Both successes, and limitations, of current computational theories will be considered.

4 credits
Cross-listed with:
COGS-6230 Human & Machine Learning
Prereqs:
none

# Past Term Data
Offered
Not Offered
Offered as Cross-Listing Only
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| Spring | Summer | Fall
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| (Session 1) | (Session 2) |
2026 |  |  |

2025

Human & Machine Learning (4c)

- Christopher Robert Sims

Seats Taken: 18/19

2024

Human & Machine Learning (4c)

- Christopher Robert Sims

Seats Taken: 16/19

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