| Date |
Lecture |
Reading |
Notes |
| Jan 14 |
Intro; Probability Review and Bayes Nets Preview |
K & F Reader, Chap 1,2
| |
| Jan 21 |
Basic Bayes Net Learning Preview |
K & F Reader, Chap 3
| HW for Jan 28: 2.9 (there is a bug in 2.9.2), 3.1, 3.4 |
| Jan 26 |
Bayes Net Semantics, I-Maps and Factorization |
K & F Reader, Chap 3
| |
| Jan 28 |
D-separation, Representation Theorem, Minimal I-Maps |
K & F Reader, Chap 3
| HW for Feb 4: 3.11 , 3.17 |
| Feb 2 |
I-Equivalence, P-Maps |
K & F Reader, Chap 3
| |
| Feb 4 |
Markov Net Semantics |
K & F Reader, Chap 4.1-4.3
| HW for Feb 11: 4.4, 4.9 |
| Feb 9 |
Class cancelled |
K & F Reader, Chap 4.4-4.5
| |
| Feb 11 |
Markov Nets and Markov Properties |
K & F Reader, Chap 4.6
| |
| Feb 16 |
Exact Inference, Variable Elimination |
K & F Reader, Chap 9.1-9.4
| HW for Feb 23: 9.2, 9.11 |
| Feb 18 |
Variable Elimination to Junction Trees |
Generalized Distributive Law,
Aji and McEliece |
|
| Feb 23 |
Junction Trees |
K&F Reader, Chap 10.1-10.3 |
HW for Mar 2: 10.6, 10.16 |
| Feb 25 |
Junction Trees cont., Variational Inference |
K&F Reader, Chap 10.4, 11.1-11.2 |
|
| Mar 2 |
Variational Inference: Mean Field, Loopy |
K&F Reader, Chap 11.1-11.3 |
|
| Mar 4 |
Loopy BP |
K&F Reader, Chap 11 |
Project Proposal Due |
| Mar 9 |
Spring Break! |
|
|
| Mar 11 |
Spring Break! |
|
|
| Mar 16 |
Midterm Review |
HW solutions |
|
| Mar 18 |
Midterm Exam |
midterm |
|
| Mar 23 |
Generalized BP |
K&F Reader, Chap 11 |
HW for Mar 30: hw6.pdf |
| Mar 25 |
Sampling: Overview, Forward, Likelihood Weighted |
K&F Reader, Chap 12.1.-12.2 |
|
| Mar 30 |
Importance Sampling |
K&F Reader, Chap 12.3 |
HW for April 6: 12.4,12.5 |
| Apr 1 |
MCMC, Gibbs and Metropolis Hastings |
|
|
| Apr 6 |
Rao-Blackwellization, Temporal Models, Particle Filters |
K&F Reader, Chap 15.3 |
HW for April 15: 15.7, 13.5 |
| Apr 8 |
MAP Inference, LP/Graphcut |
K&F Reader, Chap 13.5-13.6,
Graph Cuts |
Project Milestone Due |
| Apr 13 |
Learning: Overview, Parameters and Structure |
K&F Reader, Chap 16 |
|
| Apr 15 |
Parameter Estimation and Structure in BNs: ML, Bayesian |
K&F Reader, Chap 17.1-17.4, Chap 18.1-18.4 |
|
| Apr 20 |
Structure Selection, Parameter Learning in MNs |
K&F Reader, Chap 20.1-20.3 |
|
| Apr 22 |
Learning with Partially Observed Data |
K&F Reader, Chap 19.1-19.2 |
|
| Apr 27 |
Class moved to Friday |
|
|
| May 1 |
Poster Presentations + Lunch |
12:00-1:30pm in Levine 307 |
|
| May 4th |
Final Exam |
12:00-2:00pm, Towne 309 |
|
| May 11th |
Final Report Due |
|
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