Human and
Machine Learning
Learn how the mind works by building it — from Bayesian inference and causal reasoning to reinforcement learning and neural networks, with hands-on probabilistic programming.
Learn how the mind works by building it — from Bayesian inference and causal reasoning to reinforcement learning and neural networks, with hands-on probabilistic programming.
A graduate seminar bridging cognitive science, machine learning, and probabilistic programming.
Build intuition for probability from counting to conjugate models, hierarchical Bayes, and Bayesian nonparametrics.
Write probabilistic programs from Week 2 onward. Model, condition, and run inference using GenJAX on JAX — no toy examples.
Causal reasoning, generalization, reinforcement learning, and social cognition — framed as computational-level theories of the mind.
Bridge to transformers, scaling laws, RLHF, and AI alignment — see where classical models meet modern deep learning.
A free, open-source textbook on probability and probabilistic computing that grows alongside the course.
Weekly reflections, in-class exercises, and collaborative problem-solving — not just lectures.
A Narrative Introduction to Probability covers discrete & continuous probability, Bayesian learning, mixture models, and probabilistic programming with GenJAX — all with interactive notebooks.
Read the Textbook GitHub Repo12 sessions, Fridays. Topics build cumulatively; GenJAX integration is woven throughout.
| Wk | Date | Topic | |
|---|---|---|---|
| 1 | Apr 17 | Introduction & Basic Bayes PDF | T1 Ch 1-3 T2 Ch 0-1 |
| 2 | Apr 24 | Basic Bayes cont'd PDF | T1 Ch 4 T3 Ch 1 T3 Ch 2 T2 Ch 0 T2 Ch 2 |
| May 1 | No class | ||
| May 8 | No class (holiday) | ||
| 3 | May 15 | Conjugate Bayes & Topic Models PDF | T3 Ch 4 T1 Ch 6 T2 Ch 2 |
| 4 | May 22 | Generalization & Hierarchical Bayes PDF | T3 Bayesian Learning T3 Mixture Models T3 Generalization T3 Hierarchical Bayes T2 Conditioning |
| 5 | May 29 | Hierarchical Bayes & Bayes Nets | T3 Mixture Models T3 Hierarchical Bayes T2 Building Models |
| 6 | Jun 5 | Markov Chains & Networks | GenJAX |
| 7 | Jun 12 | Monte Carlo Methods | |
| 8 | Jun 19 | SDT, MDPs & Reinforcement Learning | |
| 9 | Jun 26 | Inverse Reinforcement Learning | GenJAX |
| 10 | Jul 3 | Bayesian Nonparametrics | GenJAX |
| 11 | Jul 10 | Deep Neural Networks | GenJAX |
Full breakdown on the syllabus.
All four assignments are completed in GenJAX.