IFT 6269 : Probabilistic Graphical Models - Fall 2017

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

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 2016 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) or the Deep Learning book (DL). Related past ‘‘scribe notes’’ from the class that I taught in Paris are given for now, and will be updated with the scribe notes as I get them (if I get some).

Date Topics Related chapters
Scribbled notes
Scribe notes Homework milestones
Sept 5 Set-up & overview intro slides
lecture1
Isabela Albuquerque
lecture1.pdf
source
Sept 8 Probability review 2.1.1
DL: 3 (nice and gentle)
KF: 2.1 (more rigorous)
lecture2
William Léchelle (Fa16)
lecture2.pdf
source
Sept 12 Parametric models
Frequentist vs. Bayesian
5
lecture3
Hwk 1 out
Sept 15 Bayesian (cont.)
Maximum likelihood
lecture4 MVA lecture1
Sept 19 Statistical decision theory 1.3 in Bickel & Doksum
lecture5
Sept 22 Properties of estimators
Linear regression
Logistic regression
old lecture5
6, 7
old lecture6
MVA lecture2
Sept 26 Logistic regression
Optimization
Kernel trick
7
old lecture7
Hwk 1 due
Hwk 2 out
data.zip
Sept 29 Generative classification (Fisher)
Derivative tricks for Gaussian MLE
K-means
10
old lecture8
MVA lecture3
Oct 3 Gaussian mixtures
EM
10, 11
old lecture9
Oct 6 Graph theory
Directed graphical models
2
old lecture10
MVA lecture4
Oct 10 Directed graphical models (cont.)
Undirected graphical models
2
old lecture11
Hwk 2 due
Hwk 3 out
Oct 13 Undirected graphical models (cont.)
Inference: elimination algorithm
2, 3
old lecture12
Oct 17 Inference: sum-product alg.
(Inference: junction tree alg.)
4, (17)
old lecture13
MVA lecture7
Oct 20 HMM and EM 12
old lecture14
Oct 24 Break: look at projects Hwk 3 due
Oct 27 Break: look at projects
Oct 31 Exponential families
Information theory
8, 19
old lecture15
MVA lecture5 Hwk 4 out
Nov 3 Exponential families (cont.)
duality
19
old lecture16
MVA lecture6
Nov 7 Gaussian networks 13
old lecture17
Project: team formed
Nov 10 Factor analysis, PCA, CCA
(Kalman filter)
14 , (15)
old lecture18
Nov 14 Sampling 21
old lecture19
MVA lecture8 Hwk 4 due
Hwk 5 out
Nov 17 Sampling (cont.) MCMC 21
old lecture20
Nov 21 Variational methods old lecture21
Nov 24 Estimation in graphical models
Bayesian methods
Model selection
9
5
26
old lecture22
MVA lecture10
Nov 28 Non-parametric models 25 Hwk 5 due
Project: 1 page
progress report due
Dec 1 Topic TBD
Dec 5 No lecture this week
work on your project!
Dec 12 Poster presentation
1:30pm-4:30pm
mezzanine of Jean-Coutu atrium
Take-home final out
Dec 20 Project report due
Take-home final due


Last modified: 2017-09-21 20h30