IFT 6269 : Probabilistic Graphical Models - Fall 2023

Last year version: Fall 2022

Description

This course provides a unifying introduction to statistical modeling of multidimensional data through the framework of probabilistic graphical models, together with their associated learning and inference algorithms.

People

Class info (hybrid lectures)

The lectures are taught in hybrid mode: the lectures will be given at the Mila Auditorium 2, 6650 rue Saint-Urbain, but also connected synchronously on Zoom (see info on Studium). They will also be recorded for further review or for those in remote time zones.

Tentative content

(Detailed outline below)

Evaluation

Prerequisites

Textbook

Homework logistics

Detailed outline (updated often)

Below is a draft detailed outline that will be updated as the class goes on. For now it is the outline recopied from the Fall 2021 version of this class, with the links to the relevant old scribbled notes that will be updated with the new ones gradually. The related chapters in Mike's book are given (but note that they do not exactly correspond with the class content), and also sometimes pointers to the Koller and Friedman book (KF), the Deep Learning book (DL) or the Bishop's book (B). Related past ‘‘scribe notes’’ from the previous instantiations of this class as well as (for later lectures) the class that I taught in Paris are given for now, and will be updated with the more updated scribe notes if needed (and I get some).

Date Topics Related chapters
Scribbled notes
Recordings
Scribe notes Homework milestones
Sept 5 (Tu) Set-up & overview lecture1
recording
Isabela Albuquerque (Fa17)
lecture1.pdf
source
Sept 7 (Thurs) Probability review 2.1.1
DL: 3 (nice and gentle)
KF: 2.1 (more rigorous)
lecture2
recording
William Léchelle (Fa16)
lecture2.pdf
source
Sept 12 (Tu) Prob. review (cont.)
Parametric models
5
lecture3
recording
Philippe Brouillard and Tristan Deleu (Fa17)
lecture3.pdf
source
Hwk 1 out
(hwk 1 source)
Sept 14 (Thurs) Frequentist vs. Bayesian
Maximum likelihood
lecture4
recording
Philippe Brouillard and Tristan Deleu (Fa17)
lecture4.pdf
source
Sept 19 (Tu) MLE (cont.)
Statistical decision theory
1.3 in Bickel & Doksum
Bias-variance tradeoff: 7.3 in Hastie's book
lecture5
recording
Sébastien Lachapelle (Fa17)
lecture5.pdf
source
Sept 21 (Thurs) Properties of estimators
and MLE
lecture6
recording
same as lecture5
Sept 26 (Tu) Linear regression
Logistic regression
DL: 7.1 (l2, l1-reg.)
6, 7
lecture7
recording
Zakaria Soliman (Fa16)
lecture6.pdf
source
Hwk 1 due
Hwk 2 out
Sept 28 (Thurs) Numerical Optimization
Logistic regression (cted) and IRLS

7
DL: 4.3
Boyd's book
lecture8
recording
Eeshan Gunesh Dhekane and Younes Driouiche (Fa18)
lecture8.pdf
source
Oct 3 (Tu) Gen. classification (Fisher)
Derivative tricks for
Gaussian MLE

Kernel trick (skipped)
7
Matrix Diff. book
lecture9

recording

lecture9 2017 (kernel trick) (skipped)
Eeshan Gunesh Dhekane (Fa18)
lecture9.pdf
source
Oct 5 (Thurs) latent variable model
K-means
EM and GMM
10, 11
lecture10
recording
Ismael Martinez and Binulal Narayanan (Fa20)
lecture12-2020.pdf
source
Oct 10 (Tu) Graph Theory
Directed Graphical Models
2
lecture11
recording
Martin Weiss and Eeshan Gunesh Dhekane (Fa18)
lecture11.pdf
source
Oct 12 (Thurs) DGM (cont.)
Undirected graphical models
2
lecture12
recording
Philippe Beardsell (Fa18)
lecture12.pdf
source
Oct 17 (Tu) Break: look at projects Hwk 2 due
Hwk 3 out
(hwk 3 source)
Oct 24 (Tu) UGM (cont.) 2
lecture13
recording
lecture12 above
Oct 26 (Thurs) Inference: elimination alg.
Sum-productr alg.
3, 4
lecture14
recording
lecture13-2018.pdf
source
Sum-product: see lecture16-2020 below
Oct 31 (Tu) Max-product
Junction tree
HMM
17, 12
lecture15
recording
lecture16-2020.pdf
source
lecture15-2018.pdf
source
Nov 2 (Thurs) EM for HMM
Information theory
12, 19
lecture16
recording
lecture15-2018.pdf above
Nov 7 (Tu) Max entropy
MaxENT duality
8
lecture17
(KL geometry: lecture 16 2017)
recording
lecture16-2018.pdf
source
Hwk 3 due
Hwk 4 out
(hwk 4 source)
Project: team formed
Nov 9 (Thurs) Exponential families
Estimation in graphical models
8
9
lecture18
recording
lecture18-2021.pdf
source
Nov 14 (Tu) MC integration
Sampling
21
(variance reduction: see old lecture18 2017)
lecture19
recording
(new scribe notes to come!)
Nov 16 (Thurs) MCMC
Markov chains
Metropolis-Hastings
21
lecture20
recording
Nov 21 (Tu) Gibbs sampling
Variational methods
Bishop: 10.1
lecture21
recording

(skipped parts of: old lecture22 2017 for marginal polytope)
MVA lecture9
Nov 23 (Thurs) Bayesian methods
Model selection

Causality
5, 26
lecture22
recording

Causality for Machine Learning (arXiv 2019)
Elements of Causal Inference (book)
MVA lecture10
Nov 28 (Tu) Guest lecture on GFlowNets by Antonio and Juan lecture23
recording
Hwk 4 due
Hwk 5 out
(hwk 5 source)
Nov 30 (Thurs) Gaussian networks

Factor analysis, PCA, CCA
(Kalman filter)
VAE
lecture24
recording

13
old lecture17 Fa2016
14 , (15)
old lecture18 Fa2016
VAE – DL: 20.10.3
MVA lecture6.5
MVA lecture7.3
Project: 1 page
progress report due
(skipped this year) (Guest lecture on causality by
Sebastien Lachapelle)
old lecture24
old recording
Dec 5 (Tu) Non-parametric models: Gaussian processes
Dirichlet processes
25
lecture25
recording

GP book
DP tutorial
lecture26-2020.pdf
source
Dec 7 (Thurs) No lecture: work on hwk 5 or project!
Dec 14 (Thurs) Take-home final out
Dec 19 (Tu) Poster presentation
2:00pm-5:00pm
Dec 22 (Fri) Hwk 5 due
Project report due
Take-home final due