| Dec 19 | Introduction | Koller ch.2 | Lecture 1 |
| Dec 23 | Probability review, Bayesian networks | Koller ch.3 | Lecture 2 |
| Dec 27 | Bayesian networks (cont.) | Koller ch.3 | Lecture 3 |
| Jan 6 | Parameter estimation | Koller ch.17.1-17.2 | Lecture 4 |
| Jan 10 | Gaussian BN, Bayesian statistics | Koller ch.17.2-17.3 | Lecture 5, Homework 1 |
| Jan 13 | Bayesian parameter estimation | Koller ch.17.4 | Lecture 6 |
| Jan 17 | Constraint-based learning | Koller ch.18.1-18.2 | Lecture 7, Homework 2 |
| Jan 20 | Score-based learning | Koller ch.18.3 | Lecture 8 |
| Feb 3 | Bayesian score, Structure search | Koller ch.18.3-18.4 | Lecture 9 |
| Feb 4 | Structure search, Missing data | Koller ch.18.4, 19.1 | Lecture 10 |
| Feb 7 | MAR, Identifiability | Koller ch.19.1 | Lecture 11 |
| Feb 10 | Gradient ascent, EM algorithm | Koller ch.19.2 | Lecture 12, Homework 3 |
| Feb 14 | Basic information theory | Bishop ch.1.6 | Lecture 13 |
| Feb 24 | Latent variable models, GMM, Variable elimination | Bishop ch.1.6, Koller ch.9.2-9.3 | Lecture 14 |
| Feb 25 | Markov random fields | Koller ch.4.1, 4.3, 4.6 | Lecture 15 |
| Feb 28 | MRF parameter estimation, MCMC | Koller ch.20.2, 20.3, 20.5, Bishop ch.11.2 | Lecture 16 |
| Mar 6 | Ising models | Murphy ch.19.4.1 | Lecture 17 |
| Mar 13 | State space models | Murphy ch.18.2.4 | Lecture 18 |
| Mar 16 | Hierarchical models | Murphy ch.5.5, 24.2.5 | Lecture 19 |
| Mar 19 | Variational autoencoder | Ermon's VAE note | Slides |