Probabilistic Models of Cognition

4 credits

Computational theories of human cognition, particularly Bayesian approaches. Techniques may include directed graphical models; hierarchical models; nonparametric models; probabilistic programming languages. Topics may include decision-making; perception; causal reasoning; categorization; language. Cross-listed with LIGN 228. Recommended preparation: Previous experience with statistics, machine learning, and/or functional programming helpful. Prerequisites: graduate standing.