CS289 Fall 2015

Textbooks

• Elements of Statistical Learning (ESL)
• An Introduction to Statistical Learning with Applications in R (ISLR)

Lectures, Discussions, Readings

Date	Topics	Handouts	Book	Other Readings
8/27	Introduction	lecture	ISLR 1
9/1	Linear models	lecture	ISLR 2	Linear discriminant and support vector classifiers
9/3	Support Vector Machines	lecture	ISLR 2 and 9	Introduction to Large Margin Classifier
9/4	Discussion 1	handout, solutions
9/8	Risk minimization, Learn opt. margin Perceptron via gradient descent	lecture		Large scale ML with SGD
9/10	Shrinkage	lecture	ISLR 6.2
9/11	Discussion 2	handout, solutions, slides
9/15	Bayesian decision theory and Logistic regression	lecture	ISLR 4
9/17	Ridge regression	lecture	ISLR 3	Kernel Ridge Regression
9/18	Discussion 3	handout, solutions, slides
9/22	Kernel methods	lecture		Kernel methods (chatper 2 and 3)
9/24	Performance evaluation	lecture	ISLR 5
9/25	Discussion 4	handout, solutions, slides
9/29	Model selection (1)	lecture
10/1	Model selection (2)	lecture	ISLR Ch 6
10/2	Discussion 5	handout, solutions, slides
10/6	Gaussian classifier	lecture
10/8	LDA	lecture	ISLR Ch 4, Ch 10.2 for PCA
10/9	Discussion 6	handout, solutions, slides
10/13	Gaussian mixtures	lecture
10/15	Gaussian processes	lecture		GP tutorial
10/16	Discussion 7	handout, solutions, slides
10/20	Non-parametric methods	lecture
10/22	Curse of dimensionality	lecture
10/23	Discussion 8	handout, solutions
10/27	Midterm	solutions
10/29	Decision Trees	lecture	ISLR 8.1
11/30	Discussion 9	handout, solutions
11/3	Decision Trees #2	lecture	ISLR 8.1
11/5	Ensemble Methods	[lecture](./lecture/Ensemble Methods.pdf)	ISLR 8.2
11/6	Discussion 10	handout, solutions
11/10	Neural Networks	lecture		A chapter from Daume's almost-finished ML textbook, A new online neural network textbook by Nielsen, Neural Networks Demystified
11/12	Training Neural Nets	lecture
11/17	Convolutional Neural Nets	lecture		Convolutional Networks, Convolutional Neural Network
11/19	Clustering	lecture	ISL 10.1, 10.3; ESL 14.3
11/20	Discussion 11	handout, solutions
11/24	PCA, collaborative filtering	lecture	ESL 6.6.1, 14.2-14.2.3; ISL 10.2
12/1	Finish PCA, density estimation, associative rules	[lecture](./lecture/Mode Finding.pdf)
12/4	Discussion 12	handout, solutions

Assignments

Due	Topic
9/11	SVM
9/24	Probability, Linear Algebra, Matrix Calculus
10/6	Linear and Logistic Regression
10/18	Gaussian Classifiers
11/9	Decision Trees and Random Forests
11/24	Neural Networks
12/4	Unsupervised Learning