Computer Science 446

Machine Learning

Instructor: Dan Gildea office hours M/W 1-2pm

TAs:

Prereqs: Probability, Linear Algebra, Vector Calculus.

The following are useful references in addition to the reading material assigned for each class:

Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach.
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Larry Wasserman, All of Statistics, 2004.

On	we will cover	which means that after class you will understand	if before class you have read
2/2	Regression and Classification	constrained optimization, perceptron	bishop 1.2, 1.4, app E
2/4	Logistic Regression	stochastic gradient descent	bishop 4.3
2/9	Backpropagation	DP for gradient descent	bishop 5.1, 5.2, 5.3
2/11	Deep Learning	drop-out	Krizhevsky 2012
2/16	Support Vectors	max-margin	bishop 3.1, 4.1
2/18	Support Vectors	strong duality	bishop 7.1
2/23	Support Vectors	the kernel trick	bishop 6.1, 6.2
2/25	Hidden Markov Models	forward-backward	bishop 13.2
3/2	Graphical Models	bayes ball
3/4	Probabilistic Inference	message passing	bishop 8.4
3/9	Tree decomposition	cyclic graphs	koller and friedman
3/11	Tree decomposition cont'd	vertex elimination
3/16	Review
3/18	Midterm
3/23	Midterm Solutions
3/25	Fairness in machine learning		Kleinberg, Corbett-Davies
3/30	no class
4/1	Expectation Maximization	L = Q + H + D	bishop 9
4/6	Expectation Maximization	mixture of gaussians	bishop 9
4/8	EM for HMM	minimum bayes risk
4/13	Sampling	Markov Chain Monte Carlo	bishop 11.2
4/15	Metropolis Hastings	detailed balance	bishop 11.2
4/20	Gibbs sampling	annealing	bishop 11.3
4/22	Learning Theory	PAC	Kearns and Vazirani
4/27	Gradient Descent	SGD convergence	Ruder 2016
4/29	GAN/VAE	deep generative models	Goodfellow et al. 2014, Kingma and Welling 2014
5/4	Reinforcement Learning	q-learning	sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1
5/6	Review	come to class with questions!

gildea @ cs rochester edu

April 27, 2021