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CSC412/2506 Winter 2019: Probabilistic Learning and Reasoning

Overview

The language of probability allows us to coherently and automatically account for uncertainty. This course will teach you how to build, fit, and do inference in probabilistic models. These models let us generate novel images and text, find meaningful latent representations of data, take advantage of large unlabeled datasets, and even let us do analogical reasoning automatically. This course will teach the basic building blocks of these models and the computational tools needed to use them.

Where and When

Course information sheet

MarkUs

Marking Scheme

Tentative Schedule

Week 1

Lecture: Introduction (Jan 8)

Tutorial: None

Reading:


Week 2

Lecture: Basic Classifiers (Jan 16)

Tutorial: Basic Supervised Learning and Probability (Jan 17)

Reading:


Week 3

Assignment 1 Due Feb 8 at 11:59pm

LaTeX Template for Solutions and LaTeX Style File

Lecture: Directed Graphical Models (Jan 22)

Tutorial: Stochastic Optimization (Jan 24)

Reading:


Week 4

Lecture: Undirected Graphical Models (Jan 29)

Tutorial: Automatic Differentiation (Jan 31)

Reading:


Week 5

Lecture: Exact Inference (Feb 5)

Tutorial: Markov Random Fields (Feb 7)

Reading:

Assignment 1 Due (Feb 8)

Sample Midterm: Sample Problems for the Midterm


Week 6

Lecture: Variational Inference (Feb 12)

Lecture Slides Slimmed: Variational Inference (Thanks Trevor Ablett)

Tutorial: Midterm (Feb 14)

Reading:


Week 7

Reading Week: No Lecture or Tutorial


Week 8

Assignment 2 Due March 15 at 11:59pm

Lecture: Sampling and Monte Carlo Methods (Feb 26)

Tutorial: More on Exact Inference (Feb 28)

Reading:


Week 9

Lecture: Sequential Data and Time-Series Models (Mar 5)

Tutorial: Gradient-based MCMC(Mar 7)

Lecture Readings:


Week 10

Lecture: Stochastic Variational Inference (Mar 12)

Tutorial: Gradient-based Optimization for Discrete Distributions (Mar 14 - Slides based on Chris Maddison's Field's Talk)

Reading:

Tutorial Readings:


Week 11

Lecture: Variational Autoencoders (Mar 19)

Tutorial: Practicalities of SVI (Mar 21)

Reading:

Fun Extensions


Week 12

Lecture: Gaussian Processes (Mar 26)

Tutorial: Expectation Maximization (Mar 28)


Week 13

Lecture: Generative Adversarial Networks (Apr 2)

Tutorial: Bayesian Optimization (Apr 4)