Computer Science 246/446

Machine Learning

Spring 2018

Instructor: Dan Gildea office hours Tu/Th 2-3pm Wegmans Hall 3019

TAs:

Location: M/W 9-10:15am, Morey 321

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
1/17 Regression and Classification constrained optimization, perceptron bishop 1.2, 1.4, app E
1/22 Support vectors stochastic gradient descent bishop 3.1, 4.1
1/24 Logistic Regression maximum entropy bishop 4.3
1/29 Backpropagation DP for gradient descent bishop 5.1, 5.2, 5.3
1/31 Deep Learning drop-out Krizhevsky 2012
2/5 Support Vectors strong duality bishop 7.1
2/7 Support Vectors the kernel trick bishop 6.1, 6.2
2/12 Hidden Markov Models forward-backward bishop 13.2
2/14 Graphical Models bayes ball
2/19 Probabilistic Inference message passing bishop 8.4
2/21 Tree decomposition cyclic graphs koller and friedman
2/26 Tree decomposition cont'd vertex elimination
2/28 Review
3/5 Midterm
3/7 Midterm Solutions
3/19 Expectation Maximization L = Q + H + D bishop 9
3/21 Expectation Maximization mixture of gaussians bishop 9
3/26 EM for HMM minimum bayes risk
3/28 Sampling Markov Chain Monte Carlo bishop 11.2
4/2 Metropolis Hastings detailed balance bishop 11.2
4/4 Gibbs sampline annealing bishop 11.3
4/9 Learning Theory PAC Kearns and Vazirani
4/11 Learning Theory VC dimension
4/16 Optimization SGD convergence Ruder 2016
4/18 PCA Eigenvectors
4/23 Reinforcement Learning q-learning sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1
4/25 Something fun
4/30 Review come to class with questions!
Final Exam: Tuesday May 8, 8:30-11:30am, Douglass Ballroom.

Grading

  • Homeworks: 50%
  • Final exam: 30%
  • Midterm: 20%
gildea @ cs rochester edu
May 7, 2018