Lecture Slides

• Introduction Readings: Mitchell, Chapter 1.

• Inductive Learning Readings: Mitchell, Chapter 2.

• Decision Tree Induction Readings: Mitchell, Chapter 3.

• Point Estimation Readings: Bishop, Chapters 1 and 2; Murphy, Chapters 2 and 3. Review Probability Basics here.

• Naive Bayes Readings: Mitchell, Sections 6.9-6.10; Murphy, Section 3.5

• Logistic Regression Readings: Tom Mitchell book chapter; Andrew Ng's notes on Linear Regression

• Error-Driven Linear Classifiers Readings: Mitchell, Chapter 4, Optional: Perceptron proofs

• Neural Networks, Readings: Mitchell, Chapter 4; Bishop Chapter 5; Deep learning book, chapter 6.

• Support Vector Machines, Readings: SVM tutorial by Chris Burges (Sections 3 and 4), Bishop, Chapter 7. Andrew Ng's notes

• Useful Optimization Theory for SVMs

• Instance Based Learning, Readings: Mitchell, Chapter 8. Tutorial on KD-trees

• Bias/Variance Tradeoff, Boosting and Bagging, Readings: Bishop, Chapter 14. Duda, Hart and Stork, Chapter 9.

• Model Selection

• Midterm Review

• Unsupervised Learning and Clustering. Readings: Bishop, Chapter 9. Duda, Hart and Stork, Chapter 10.

• Bayesian networks: Representation. Readings: Murphy chapter 10. My Notes on Bayesian networks

• Bayesian networks: Inference. Readings: Bishop, Chapter 8. Murphy Chapter 20. My Notes on Bayesian networks

• Parameter Learning in Bayesian networks. Readings: Murphy, Chapter 10 and 11.

• Hidden Markov models. Readings: Bishop, Chapter 13. Kevin Murphy's book chapter on HMMs (Sections 15.1-15.3).

• Computational Learning Theory. Readings: Mitchell, Chapter 7.

• RECAP. Readings: A Few Useful Things to Know about Machine Learning by Pedro Domingos.

Some material on the slides is courtesy of Pedro Domingos, Carlos Guestrin, Luke Zettlemoyer, Raymond Mooney, Dan Weld and Vincent Ng.