STA D68H (Winter 2014): Advanced Machine Learning and Data Mining

Instructor :

Russ Salakhutdinov, Office: IC462
Email: rsalakhu [at] utstat [dot] toronto [dot] edu
Lectures: Mondays 2-5pm
First Lecture: : Monday, Jan 6, 2014 at IC 328.
Office hours: Mondays, 12:00pm - 1:00pm.

Midterm is on Monday Feb 24, 2014:
You can use a nonprogrammable calculator and an 8 by 11 inch Crib Sheet - Single-sided .

Final is on TBD
You can use a nonprogrammable calculator and an 8 by 11 inch Crib Sheet - Double-sided .

Course Outline:

This course covers some of the theory and methodology of statistical aspects of machine learning. The preliminary set of topics to be covered include:

Linear methods for regression, Bayesian linear regression
Linear models for classification
Probabilistic Generative and Discriminative models
Regularization methods
Model Comparison and BIC
Neural Networks
Radial basis function networks
Kernel Methods, Gaussian processes, Support Vector Machines
Mixture models and EM algorithm
Graphical Models and Bayesian Networks

Prerequisite: STAC58H3 and STAC67H3

Text :
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer.

You can also use these books for additional reference:

Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) The Elements of Statistical Learning
David MacKay (2003) Information Theory, Inference, and Learning Algorithms

Contact Information

Email: rsalakhu [at] utstat [dot] toronto [dot] edu
Office: IC462

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