| Week | Tutorials - Wednesdays | Lectures - Mondays |
| 18/5 | * HMM- BF algorithm | * Chapter 3, pp. 77-96 |
| 25/5 | External lecture | * Chapter 3, pp. 96-110 |
| 1/6 | + P1. Marginal Independence | * Chapter 3, pp. 111-131 |
| 8/6 | + P2. Conditional Independence | - Vacation - (SHAVOHOT) |
| 15/6 | + P12. Chordal Graphs | * Exact Inference and Combinatorial Optimization |
| 22/6 | + P13. Bucket Elimination | * P3. D-separation |
| 29/6 | + P14. Message Passing | + P4. Treewidth; P5. Feedback Vertex Set |
| 6/7 | + P15. Clique Tree algorithm | *Introduction to Bayesian Statistics |
| 13/7 | * HMM- Viterbi algorithm | *Learning Bayesian Networks (P6,P7) |
| 20/7 | + P17. Variational Inference | *Tree Augmented Networks (P8) |
| 27/7 | + P18. Bayesian Sets | + P9. EM and Structural EM |
| 3/8 | TBD | + P10. Searching for Bayesian networks |
| 10/8 | - No class - | + P11. Loopy Belief Propagation |
* Classes given by course staff.
+ Lectures taken by students.
- Lectures awaiting an additional student.