Computer Science 246/446

Machine Learning

Spring 2017

Instructor: Dan Gildea office hours Tu/Th 2-3:15pm

TAs:

  • Linfeng Song office hours M/W 10:30-11:30 CSB 631
  • Joseph Majesky office hours F 10-11 CSB 727
  • Chester Holtz office hours W 12:15-1:15 CSB 727
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
1/18 Regression and Classification constrained optimization, perceptron bishop 1.2, 1.4, app E
1/23 Support vectors stochastic gradient descent bishop 3.1, 4.1
1/25 Logistic Regression maximum entropy bishop 4.3
1/30 Backpropagation DP for gradient descent bishop 5.1, 5.2, 5.3
2/1 Deep Learning drop-out Krizhevsky 2012
2/6 Support Vectors strong duality bishop 7.1
2/8 Support Vectors the kernel trick bishop 6.1, 6.2
2/13 Hidden Markov Models forward-backward bishop 13.2
2/15 Graphical Models bayes ball
2/20 Probabilistic Inference message passing bishop 8.4
2/22 Tree decomposition cyclic graphs koller and friedman
2/27 Tree decomposition cont'd vertex elimination
3/1 Review
3/6 Midterm
3/8 Midterm Solutions
3/20 Expectation Maximization L = Q + H + D bishop 9
3/22 Expectation Maximization mixture of gaussians bishop 9
3/27 HMM cont'd minimum bayes risk
3/29 Learning Theory PAC Kearns and Vazirani
4/3 Learning Theory VC dimension
4/5 Sampling Markov Chain Monte Carlo bishop 11.2
4/10 Sampling cont Gibbs Sampling bishop 11.3
4/12 Optimization Newton's method, DFP Nocedal 8.1, 9.1
4/17 Optimization, cont'd LBFGS Nocedal 8.1, 9.1
4/19 PCA Eigenvectors
4/24 Reinforcement Learning q-learning sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1
4/26 Random graphs Robbins-Monro Snijders (2002)
5/1 Review come to class with questions!
5/3 Review
Final Exam: Wednesday May 10, 8:30-11:30am, Morey 321.

Grading

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

gildea @ cs rochester edu
April 25, 2017