STA 4273H Winter 2015 - Lectures
Video Archive here.
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 ]
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STA 4273H (Winter 2015): Large Scale Machine Learning || http://www.utstat.toronto.edu/~rsalakhu/STA4273_2015/
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