CSC411 (Fall 2015): Machine Learning - Lecture Schedule

Sep 17
Lecture 1 -- Machine Learning:
Introduction to Machine Learning, Regression [pdf]
Reading: Bishop, Chapter 1.0-1.1; 3.1
Tutorial: Review on Probability [pdf]
Sep 24
Lecture 2 -- Classification, Logistic Regression, [pdf] [pdf]
Reading: Bishop, Pages 179-195, 203-207.
Tutorial: Optimization [pdf]
Oct 1
Lecture 3 -- kNN and Decision Trees, [pdf] [pdf]
Reading: Bishop: Pages 120-127
Tutorial: kNN and Decision Trees [pdf]
Oct 8
Lecture 4 -- Probabilistic Classifiers [pdf] [pdf] [pdf]
Reading: Bishop chapters 4.1.2, 4.2.2, 4.3.4, and pages 380-381.
Tutorial: Naive Bayes and Gaussian Bayes Classifier [pdf]
Oct 15
Lecture 5 -- Neural Networks [pdf]
Tutorial: Neural Networks [pdf]
Reading: Bishop 5.1 - 5.3
Oct 22
Lecture 6 -- Clustering [pdf]
Midterm review [pdf]
Reading: Bishop 9.1
Oct 29
Lecture 7 -- Mixture of Gaussians [pdf]
Tutorial: Clustering and MoG [pdf]
Reading: Bishop 9.2 - 9.3
Nov 5
Lecture 8 -- PCA and Autoencoders [pdf]
Tutorial: PCA and Autoencoders [pdf]
Reading: Bishop 12.1
Nov 12
Lecture 9 -- SVMs and Kernels [pdf]
Tutorial: SVMs [pdf]
Reading: Bishop: Chapter 7, pages 325-337
Nov 19
Lecture 10 -- Ensemble Methods [pdf]
Reading: Bishop 14.2 - 14.3
Tutorial: Ensemble Methods [pdf]
Nov 26
Lecture 11 -- Bayesian Models, Reinforcement Learning I [pdf] [pdf]
Reading: Bishop: Chapter 3.3
RL Tutorial: [pdf]
Dec 2
Lecture 12 -- Reinforcement Learning II, Final Review [pdf]
RL Tutorial: [pdf]