STA 4273H (Winter 2015): Large Scale Machine Learning

Lectures: Mondays 11:00am to 2:00pm in Stewart Library, Fields Inst.
Instructor :
  • Russ Salakhutdinov, Office: Pratt Building, Room 290F,
  • Email: rsalakhu [at] cs [dot] toronto [dot] edu

  • Lectures: Mondays 11:00am to 2:00pm
  • First Lecture: Jan 5, 2015.
  • Office hours: Mondays 2-3pm.

Live Streaming URL here.
Video Archive here.
Lecture slides and video recordings here.

Marking Scheme:

  • 2 assigments: 40%
  • Final Research Project 60% (project proposal 10%, oral presentation 10%, final project writeup 40%)
  • For non-UofT students: Grade: Pass/Fail based only on the Final Project.

Course Outline:

Statistical machine learning is a very dynamic field that lies at the intersection of Statistics and computational sciences. The goal of statistical machine learning is to develop algorithms that can "learn" from data using statistical and computational methods. Over the last decade, numerous research fields, such as computational biology, neuroscience, artificial intelligence, data mining, signal processing, finance have been strongly influenced by advances in machine learning.

This is an advanced graduate course, designed for Master's and Ph.D. level students, and will assume a reasonable degree of mathematical maturity. Specific topics to be covered include:

  • Linear methods for regression/classification
  • Model assessment and selection
  • Graphical models, Bayesian networks, Markov random fields, conditional random fields
  • Approximate variational inference, mean-field inference
  • Basic sampling algorithms, Markov chain Monte Carlo, Gibbs sampling, and Metropolis-Hastings algorithm
  • Mixture models and generalized mixture models
  • Unsupervised learning, probabilistic PCA, factor analysis, independent component analysis.

Prerequisite : Knowledge of statistical inference, probability theory, and linear algebra at the advanced undergraduate level, and some basic programming skills in R or Matlab. STA414/2104 is a plus, but is not required.

Books :

Contact Information

Email: rsalakhu [at] cs [dot] toronto [dot] edu

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