STA 4273H Winter 2015 - Lectures

Lecture Schedule

• Lecture 1 -- Machine Learning:
  Introduction to Machine Learning, Linear Models for Regression (notes [pdf], [Video] ) 
  Reading: Bishop, Chapter 1: sec. 1.1 - 1.5. and Chapter 3: sec. 1.1 - 1.3. 
  Optional: Bishop, Chapter 2: Backgorund material; 
  Hastie, Tibshirani, Friedman, Chapters 2 and 3.

   

• Lecture 2 -- Bayesian Framework:
  Bayesian Linear Regression, Evidence Maximization. Linear Models for Classification. (notes [pdf], [Video] ) 
  Reading: Bishop, Chapter 3: sec. 3.3 - 3.5. Chapter 4. 
  Optional: Radford Neal‘s NIPS tutorial on Bayesian Methods for Machine Learning: [pdf]). Also see Max Welling‘s notes on Fisher Linear Discriminant Analysis [pdf ]

STA 4273H (Winter 2015): Large Scale Machine Learning || http://www.utstat.toronto.edu/~rsalakhu/STA4273_2015/