STA414 / STA2104 Winter 2019

Statistical Methods for Machine Learning II

This course introduces machine learning to students with a statistical background. Besides teaching standard methods such as logistic and ridge regression, kernel density estimation, and random forests, this course course will try to offer a broader view of model-building and optimization using probabilistic building blocks.

What you will learn:

Instructors:

Syllabus

Piazza

Teaching Assistants:

Location:

This course has two identical sections each week

Reading

No required textbooks.

Tentative Schedule


Week 1 - Jan 7th & 8th - Overview


Week 2 - Jan 14th & 15th - Probabilistic Models


Week 3 - Jan 21st & 22nd - Regularization and Bayesian Methods


Week 4 - Jan 28th & 29th - Classification


Week 5 - Feb 4th & 5th - Decision Theory, and Optimization


Week 6 - Feb 11th & 12th - Unsupervised learning, Latent variable models


Week 7 - Feb 18th & 19th - No class - Family Day


Week 8 - Feb 25th & March 5th - Graphical models and Neural networks

Readings:


Week 9 - March 4th & 7th - Autodiff and Markov chain Monte Carlo

For the evening session, this lecture will be held on Thursday March 7th from 7pm to 10pm, in Lash-Miller room 161.

Readings:


Week 10 - March 11th & 12th - Variational inference

Demos:

Readings and watchings:


Week 11 - March 18th & 19th - Reinforcement learning and gradient estimation


Week 12 - March 25th & 26th - Variational autoencoders and time series models

Related reading:


Week 13 - April 1st & 2nd - Generative models II

Related reading: