Computer Science 246/446

Machine Learning

Spring 2018

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

TAs:

  • Xiangru Lian, office hours Thursday 3pm-5pm Wegmans Hall 3207 (no office hours 1/18)
  • Parker Riley, office hours TBD
  • Zhengyuan Yang, office hours Monday 2pm-4pm, Wegmans Hall 3504

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

The course is full. If spaces become available, I will add people who hand in the first homework. If you would like to try to add, write "NEED TO ADD" on the top of your assignment, along with your name, program (eg, BS in CS) and year.

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 HMM cont'd minimum bayes risk
3/28 Learning Theory PAC Kearns and Vazirani
4/2 Learning Theory VC dimension
4/4 Sampling Markov Chain Monte Carlo bishop 11.2
4/9 Sampling cont Gibbs Sampling bishop 11.3
4/11 Optimization Newton's method, DFP Nocedal 8.1, 9.1
4/16 Optimization, cont'd LBFGS Nocedal 8.1, 9.1
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: Wednesday May 10, 8:30-11:30am, Morey 321.

Grading

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

gildea @ cs rochester edu
January 18, 2018