| Topic | Week | Meeting Time |
| Introduction and Overview | (Week 1) | |
| Bayesian Network Fundamentals | (Week 1) |
| Template Models | (Week 1) |
| ML-class Octave Tutorial | (Week 1, Optional) |
Structured CPDs (Week 2)
Markov Network Fundamentals (Week 2)
Representation Wrapup: Knowledge Engineering (Week 3)
Inference: Belief Propagation, Part 1 (Week 3)
Inference: Belief Propagation, Part 2 (Week 4)
Inference: MAP Estimation, Part 1 (Week 4)
Inference: MAP Estimation, Part 2 (Week 5)
Inference: Sampling Methods (Week 5)
Inference: Temporal Models and Wrap-up (Week 6)
Decision Theory (Week 6)
ML-class Revision (Week 6, Optional)
Learning: Overview (Week 6)
Learning: Parameter Estimation in BNs (Week 7)
Learning: Parameter Estimation in MNs (Week 7)
Structure Learning (Week 8)
Learning With Incomplete Data (Week 9)
Learning: Wrapup (Week 9)
Summary (Week 9)