Computer Science 446

Machine Learning

Spring 2022

Instructor: Dan Gildea office hours M/W 1-2pm, 3019 Wegmans

TAs:

Location: Tu/Th 11:05-12:20am, Gavett 202

Prereqs: Probability, Linear Algebra, Vector Calculus.

Homeworks

Lecture notes

Required text: Christopher M. Bishop, Pattern Recognition and Machine Learning.

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.

Syllabus

Onwe will coverwhich means that after class you will understandif before class you have read
1/13 Regression and Classification constrained optimization, perceptron bishop 1.2, 1.4, app E
1/18 Logistic Regression stochastic gradient descent bishop 4.3
1/20 Backpropagation DP for gradient descent bishop 5.1, 5.2, 5.3
1/25 Deep Learning drop-out Krizhevsky 2012
1/27 Support Vectors max-margin bishop 3.1, 4.1
2/1 Support Vectors strong duality bishop 7.1
2/3 Support Vectors the kernel trick bishop 6.1, 6.2
2/8 Hidden Markov Models forward-backward bishop 13.2
2/10 Graphical Models bayes ball
2/15 Probabilistic Inference message passing bishop 8.4
2/17 Tree decomposition cyclic graphs koller and friedman
2/22 Tree decomposition cont'd vertex elimination
2/24 Review
3/1 Midterm
3/3 Midterm Solutions
3/15 Fairness in machine learning Kleinberg, Corbett-Davies
3/17 Expectation Maximization L = Q + H + D bishop 9
3/22 Expectation Maximization mixture of gaussians bishop 9
3/24 EM for HMM minimum bayes risk
3/29 Sampling Markov Chain Monte Carlo bishop 11.2
3/31 Metropolis Hastings detailed balance bishop 11.2
4/5 Gibbs sampling annealing bishop 11.3
4/7 Learning Theory PAC Kearns and Vazirani
4/12 Gradient Descent SGD convergence Ruder 2016
4/14 GAN/VAE deep generative models Goodfellow et al. 2014, Kingma and Welling 2014
4/19 Reinforcement Learning q-learning sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1
4/21 Something fun
4/26 Review come to class with questions!
Final exam: Friday May 6, 8:30-11:30am

Grading

  • Homeworks: 50%
  • Final exam: 30%
  • Midterm: 20%

gildea @ cs rochester edu
April 13, 2022