Computer Science 446

Machine Learning

Spring 2020

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

TAs:

Location: Tu/Th 11:05-12:20 Dewey 2162

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
1/16 Regression and Classification constrained optimization, perceptron bishop 1.2, 1.4, app E
1/21 Logistic Regression stochastic gradient descent bishop 4.3
1/23 Backpropagation DP for gradient descent bishop 5.1, 5.2, 5.3
1/28 Deep Learning drop-out Krizhevsky 2012
1/30 Support Vectors max-margin bishop 3.1, 4.1
2/4 Support Vectors strong duality bishop 7.1
2/6 Support Vectors the kernel trick bishop 6.1, 6.2
2/11 Hidden Markov Models forward-backward bishop 13.2
2/13 Graphical Models bayes ball
2/18 Probabilistic Inference message passing bishop 8.4
2/20 Tree decomposition cyclic graphs koller and friedman
2/25 Tree decomposition cont'd vertex elimination
2/27 Review
3/3 Midterm
3/5 Midterm Solutions
3/17 Fairness in machine learning (optional) Kleinberg, Corbett-Davies
3/19 no class
3/24 Expectation Maximization L = Q + H + D bishop 9
3/26 Expectation Maximization mixture of gaussians bishop 9
3/31 EM for HMM minimum bayes risk
4/2 Sampling Markov Chain Monte Carlo bishop 11.2
4/7 Metropolis Hastings detailed balance bishop 11.2
4/9 Gibbs sampling annealing bishop 11.3
4/14 Learning Theory PAC Kearns and Vazirani
4/16 Gradient Descent SGD convergence Ruder 2016
4/21 PCA Eigenvectors
4/23 Reinforcement Learning q-learning sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1
4/28 Review come to class with questions!
Final Exam: due noon Fri May 8.

Grading

  • Homeworks: 50%
  • Final exam: 30%
  • Midterm: 20%
gildea @ cs rochester edu
May 5, 2020