Date 
Topic 
Recommended Additional Reading 
Further Study 
Notes 
Supervised Learning 
R Jan 11

Introduction and course overview; Binary classification and Bayes error [slides] [lecture notes] 
Bishop Ch 1

Duda, Hart & Stork, Pattern Classification


T Jan 16  [Class postponed]  
R Jan 18  [Class postponed]  
T Jan 23 
Generative probabilistic models for classification 
Bishop Ch 4 
PS1 out 

R Jan 25 
Discriminative probabilistic models for classification 
Bishop Ch 4 

F Jan 26  Least squares regression  Bishop Ch 3  
On your own 
Review: Constrained optimization and Lagrangian duality 
Boyd & Vandenberghe, Convex Optimization  
T Jan 30  Support vector machines  Bishop 7.1  
R Feb 1  Kernel methods  Bishop Ch 6  
F Feb 2  Neural networks/deep learning  Bishop Ch 5  
T Feb 6 
Decision trees; Nearest neighbor methods 
Bishop 14.4, 2.5.2 

PS1 due; PS2 out 
R Feb 8  No class  
T Feb 13  Boosting  
R Feb 15  No class  
T Feb 20 
Model selection; Generalization error bounds/VC dimension; Structural risk minimization 
PS2 due; PS3 out 
Unsupervised Learning 
R Feb 22  Principal component analysis  
T Feb 27  Midterm exam (in class)  
R Mar 1  Autoencoding neural networks  
T Mar 6  No class (Spring Break)  
R Mar 8  No class (Spring Break)  
T Mar 13 
kmeans clustering; Mixture models and EM algorithm 


PS3 due; PS4 out; Project proposals due 
Probabilistic Graphical Models 
R Mar 15  Hidden Markov models  
T Mar 20  Hidden Markov models  
R Mar 22  Bayesian networks  
T Mar 27 
Markov networks; variable elimination 


PS4 due; PS5 out 
R Mar 29 
Messagepassing algorithms: sumproduct, maxsum 
Other Learning Frameworks 
T Apr 3  Online learning  
R Apr 5 
Semisupervised learning; Active learning 

T Apr 10 
Reinforcement learning 


PS5 due; PS6 out 
Additional/Advanced Topics 
R Apr 12  Structured prediction  
T Apr 17  Complex performance measures  
R Apr 19  Statistical consistency and PAC learning 
Home Stretch 
T Apr 24 
Project poster presentations 


PS6 due; Project reports due 
TBA  Final exam 