IFT 6269 : Probabilistic Graphical Models

Description

This course provides a unifying introduction to probabilistic modelling through the framework of 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. The related chapters in Mike's book are given (but note that they do not exactly correspond with the class content). 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.

Date Topics Related chapters
Scribbled notes
Scribe notes Homework milestones
Sept 2 Set-up & overview lecture1
Sept 6 Probability review 2.1.1
lecture2
William Léchelle
lecture2.pdf
source
Sept 9 Parametric models
Frequentist vs. Bayesian
5
lecture3
Sept 13 Maximum likelihood
Statistical decision theory
lecture4 MVA lecture1 Hwk 1 (updated version) out
Sept 16 Properties of estimators lecture5
Sept 20 Linear regression
Logistic regression
6, 7
lecture6
MVA lecture2
Sept 23 Logistic regression
Optimization
Kernel trick
7
lecture7
Sept 27 Generative classification (Fisher)
Derivative tricks for Gaussian MLE
K-means
10
lecture8
MVA lecture3 Hwk 1 due
Hwk 2 out
data.zip
Sept 30 Gaussian mixtures
EM
10, 11
lecture9
Oct 4 Graph theory
Directed graphical models
2
lecture10
MVA lecture4
Oct 7 Directed graphical models (cont.)
Undirected graphical models
2
lecture11
Oct 11 Undirected graphical models (cont.)
Inference: elimination algorithm
2, 3
lecture12
Hwk 2 due
Hwk 3 out
data.zip
Oct 14 Inference: sum-product alg.
(Inference: junction tree alg.)
4, (17)
lecture13
MVA lecture7
Oct 18 HMM and EM 12
lecture14
Oct 21 Exponential families
Information theory
8, 19
lecture15
MVA lecture5
Oct 25 Exponential families (cont.)
duality
19
lecture16
MVA lecture6 Hwk 3 due
Oct 28 Gaussian networks 13
lecture17
Nov 1 Cancelled
Nov 4 Factor analysis, PCA, CCA
(Kalman filter)
14 , (15)
lecture18
Nov 8 Sampling 21
lecture19
MVA lecture8 Hwk 4 out
Nov 11 Sampling (cont.) MCMC 21
lecture20
Project group formed
Nov 15 Variational methods lecture21
Nov 18 Estimation in graphical models
Bayesian methods
Model selection
9
5
26
lecture22
MVA lecture10
Nov 22 Non-parametric models 25 Hwk 4 due
Nov 25 Guest lecture: Aaron Courville Hwk 5 out
Nov 29 No lecture Project: 1 page
progress report due
Dec 6 No lecture this week
work on your project!
Dec 13 Poster presentation
1pm–3:30pm in AA6225
Take-home final out
Dec 20 Project report due
Take-home final due
Hwk 5 due


Last modified: 2016-12-12 23h30