Note: 2pp = 2 slides per page (portrait), 4pp = 4 slides per page (landscape).
| Week | Monday | Wednesday | Friday |
|---|---|---|---|
| Week #1 Jan 6 - 10 |
Lecture: Overview (2pp, 4pp), Directed Graphical Models (Introduction) (2pp, 4pp) Assignment #1 out |
Lecture: Directed Graphical Models (Reasoning Patterns, Independencies) (2pp, 4pp, audio) Reading: Section 3.2.1.2, 3.2.2, 3.2.3 |
Lecture: Directed Graphical Models (D-separation) (2pp, 4pp, audio) Reading: Section 3.3 |
| Week #2 Jan 13 - 14 |
Lecture: Directed Graphical Models (I-equivalences, Distributions and Graphs) (2pp, 4pp, audio) Genie example (HMM.xdsl) Reading: Section 3.4 (up to 3.4.3) |
Lecture: Undirected Graphical Models (Introduction to Markov Networks) (2pp, 4pp, audio) Assignment #2 Assignment #1 due Reading: 4.1, 4.2 |
Lecture: Undirected Graphical Models (Independencies) (2pp, 4pp, audio) Reading: 4.3 |
| Week #3 Jan 20 - 24 |
No class (MLK day) | Lecture: Undirected Graphical Models (Chordal graphs) (2pp, 4pp, audio) Reading: 4.4 Assignment #2 due Assignment #3 out |
Lecture: Boltzman Machines (2pp, 4pp, audio) |
| Week #4 Jan 27 - Jan 31 |
Lecture: Exact Inference (Enumeration, Variable Elimination) (2pp, 4pp, audio) Reading: Chapter 9.1-9.3 |
Lecture: Exact Inference (Variable Elimination) (2pp, 4pp, audio) Reading: Chapter 9.3 Assignment #3 due |
Lecture: Exact Inference (Complexity of Variable Elimination) (2pp, 4pp, audio) Reading: 9.3 |
| Week #5 Feb 3 - 7 |
Lecture: Exact Inference (Message Passing) (2pp, 4pp, audio) Practice Midterm Reading: 10.1, 10.2 |
Lecture: Exact Inference (Message Passing 2) (2pp, 4pp, audio) | Midterm Review (Practice Midterm Solutions) |
| Week #6 Feb 10 - 14 |
Midterm | Lecture: Approximate Inference (Sampling, Likelihood Weighting) (2pp, 4pp, audio) | Midterms returned (solutions) Project proposal due |
| Week #7 Feb 17 - 21 |
Lecture: Approximate Inference 2 (Importance Sampling) (2pp, 4pp, audio) Assignment #4 out Reading: 12.2 |
Lecture: Monte Carlo Markov Chain 1 (2pp, 4pp, audio) Reading: Chapter 12.3 |
Lecture: Monte Carlo Markov Chain 2 (2pp, 4pp, no audio available) Reading: Chapter 12.3 |
| Week #8 Feb 24 - Feb 28 |
Lecture: Variational Inference (2pp, 4pp, audio) Assignment #4 due |
Lecture: Parameter Learning 1 (2pp, 4pp, audio) | Lecture: Parameter Learning 2 (2pp, 4pp, audio) Project Checkpoint |
| Week #9 Mar 2 - 6 |
Lecture: Structure Learning (Chow Liu Trees) (2pp, 4pp, audio) | Lecture: Structure Learning (Constraint-Based Approaches) (2pp, 4pp, audio) Reading: Chapter 3.4.3, 18.1-18.2 Assignment #5 out | Lecture: Structure Learning (Structure Scoring) (2pp, 4pp, audio) Reading: 18.3, 18.4.3 |
| Week #10 Mar 9 - 13 |
Lecture: Variational Autoencoders (2pp, 4pp, audio) | Video: Tractable Probabilistic Models UAI 2019 tutorial (up to 51:23 mark) (link) Assignment #5 due |
Video: Tractable Probabilistic Models UAI 2019 tutorial (the rest) link) |
Projects are due Wed Mar 18, 2020 at 23:59:59. Please slide the writeup under my door (or email the pdf) and submit the code via the TEACH handin.