Computer Science 246/446

Machine Learning

Spring 2016

Instructor: Dan Gildea office hours M/Th 2-3:15pm
TAs: Xiaochang Peng,
Linfeng Song
Location: M/W 9-10:15am, Morey 321

Prereqs: Probability, Linear Algebra, Vector Calculus. There will be a vector calculus review Fri 1/15, 4-5pm, in CSB 209.

Homeworks

Lecture notes and other readings available through NB.

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 pseudoinverse, constrained optimization bishop 1.2, 1.4, app E
1/20 Information Theory entropy, kl-distance, coding bishop 1.6
1/25 Probabilistic Inference priors: bayesian reasoning, MAP bishop 3.4
1/27 Perceptron stochastic gradient descent bishop 3.1, 4.1
2/1 Backpropagation DP for gradient descent bishop 5.1, 5.2, 5.3
2/3 Deep Learning drop-out Krizhevsky 2012
2/8 Support Vectors strong duality bishop 7.1
2/10 Support Vectors the kernel trick bishop 6.1, 6.2
2/15 Probabilistic Inference message passing bishop 8.4
2/17 Tree decomposition cyclic graphs koller and friedman
2/22 Expectation Maximization L = Q + H + D bishop 9
2/24 Expectation Maximization mixture of gaussians bishop 9
2/29 Sampling Markov Chain Monte Carlo bishop 11.2
3/2 Review
3/14 Midterm
3/16 Learning theory
3/21 Midterm Solutions
3/23 Sampling cont Gibbs Sampling bishop 11.3
3/28 Learning Theory VC dimension Kearns and Vazirani
3/30 Logistic Regression maximum entropy bishop 4.3
4/4 Hidden Markov Models forward-backward bishop 13.2
4/6 HMM cont'd minimum bayes risk
4/11 Optimization Newton's method, DFP Nocedal 8.1, 9.1
4/13 Particle Filters slam Thrun et al. ch 4
4/18 PCA Eigenvectors
4/20 Reinforcement Learning q-learning sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1
4/25 AlphaGo Silver 2016
4/27 Review come to class with questions!
Final Exam: Wednesday May 4, 8:30-11:30am, Morey 321.

Grading

  • Final exam: 30%
  • Homeworks: 45%
  • Midterm: 20%
  • Class participation: 5%
gildea @ cs rochester edu
July 7, 2016