Machine Learning
Material covered in class so far.
Week TuesdayThursdayReadings
January 13 What is Machine Learning? Chapter 1
January 18, 20 Examples of Machine Learning Applications Apriori association rule algorithm Chapters 1, 2, 3.9
January 25, 27 PAC learning for finite hypothesis spaces
VC dimension examples
HW1 due. Quiz 1 Chapter 2
February 1, 3 Candidate Elimination algorithm Naive Bayes classifier Chapter 2, 3
February 8, 10 HW2 due. Laplace estimator, 1R classifier, LMS no class Chapter 3, 9
February 15, 17 Quiz 2. (Decision trees) Information theory and entropy (odd ball example),
Probability densities
Chapter 9, Appendix
February 22, 24 k-nearest neighbor kd-trees Chapter 8, 4
March 1, 3 Linear discrimination Support Vector Machines Chapter 10
March 8, 10 A practical guide to SVM classification SVM: Kernel functions, examples "A practical guide to Support Vector Classification" by Hsu, Chang, Lin
March 15, 17 Perceptron Dr. Freeman "Perceptron is a process of creating self-renewal", at 6pm in PSL Chapter 11
March 22, 24 Spring Break
March 29, 31 Neural networks: Gradient descent and the delta rule Neural networks: Backpropagation algorithm Chapter 11
Week TuesdayWednesdayThursday
April 5, 7 Evan "Clustering and You" Mayur "Decision Tree Methods"
April 12, 13, 14 Brian Adam Kalyan
April 19, 20, 21 Senlin Brad Mike
April 26, 28 no classes
May 3, 5 Vitus Jacob
Exam week: May 2-6