CSC 411
Lecture Slides and Recommended Readings
(courtesy of Rich Zemel, Raquel Urtasun and Sanja Fidler)

Lecture 1: Introduction

Lecture 2: Linear Regression  (Bishop: chapters 1.0-1.1,  3.1)

Lecture 3: Linear Classification  (Bishop: pages 179-195)

Lecture 4: Logistic Regression  (Bishop: pages 203-207)

Lecture 5: Non-parametric Methods  (Bishop: pages 120-127)

Lecture 6: Decision Trees

Lecture 7: Multi-class Classification  (Bishop: 4.1.2, 4.3.4)

Lecture 8: Probabilistic Classifiers I

Lecture 9: Probabilistic Classifiers II  (Bishop: pages 380-381)

Lecture 10: Neural Networks I  (Bishop: 5.1-5.3)

Lecture 11: Neural Networks II

Lecture 12: Clustering  (Bishop: 9.1)

Lecture 13: Mixture of Gaussians  (Bishop: 9.2, 9.3)

Lecture 14: PCA and Autoencoders  (Bishop: 12.1)

Lecture 15: SVM  (Bishop: chapter 7,  pages 325-337)

Lecture 16: Kernels

Lecture 17: Ensemble Methods I  (Bishop: 14.2-14.3)

Lecture 18: Ensemble Methods II

Lecture 19: Reinforcement Learning