CSC 411F, Spring 2007
Lecture Slides
(courtesy of Rich Zemel, Geoff Hinton and Radford Neal)

Lecture 1: Introduction

Lecture 2: Bayes Rule

Lecture 3: Naive Bayes Classification

Lecture 4: Nearest Neighbors

Lecture 5: Decision Trees: introduction

Lecture 6: Decision Trees: learning

Lecture 7: Neural Nets: introduction

Lecture 8: Neural Nets: perceptrons

Lecture 9: Neural Nets: backpropagation

Lecture 10: Neural Nets: applications

Lecture 11: Recurrent Networks

Lecture 12: Clustering

Lecture 13: Principle Components Analysis

Lecture 14: Multidimensional Scaling and Isomap. Reading: the original Isomap paper

Lecture 15: Reinforcement Learning

Lecture 16: Q Learning

Lecture 17: TD Learning

Lecture 18: Support Vector Machines: maximum margin

Lecture 19: Support Vector Machines: the kernel trick

Lecture 20: Bayesian Learning

Lecture 21: Bayesian Neural Nets