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.
Paper presentations, 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 Repo13 sessions, Fridays. Topics build cumulatively; GenJAX integration is woven throughout.
| Wk | Date | Topic | |
|---|---|---|---|
| 1 | Apr 17 | Introduction & Basic Bayes | T1 Ch 1-3 |
| 2 | Apr 24 | Basic Bayes cont'd | T1 Ch 4-5 GenJAX HW |
| May 1 | No class | ||
| 3 | May 8 | Conjugate Bayes & Topic Models | T3 Ch 1-3 T2 Ch 2-3 |
| 4 | May 15 | Generalization & Hierarchical Bayes | T3 Ch 4 T2 Ch 4 |
| 5 | May 22 | Hierarchical Bayes & Bayes Nets | T3 Ch 5-6 T2 Ch 5-6 |
| 6 | May 29 | Causal Bayes Nets | Causal exercises |
| 7 | Jun 5 | Markov Chains & Networks | |
| 8 | Jun 12 | Monte Carlo Methods | Sampling/inference |
| 9 | Jun 19 | SDT, MDPs & Reinforcement Learning | |
| 10 | Jun 26 | Inverse Reinforcement Learning | |
| 11 | Jul 3 | Bayesian Nonparametrics | DPMM |
| 12 | Jul 10 | Deep Neural Networks | Updated |
| 13 | Jul 17 | Ethics & Adversarial ML | Updated |
All assignments available in Python with GenJAX options.