Lectures / Schedule


Tentative Schedule (in progress)

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 Auto-encoding neural networks      
T Mar 6 No class (Spring Break)      
R Mar 8 No class (Spring Break)      
T Mar 13
 
 
 
k-means 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
 
Message-passing algorithms:
sum-product, max-sum
     
Other Learning Frameworks
T Apr 3 Online learning      
R Apr 5
 
Semi-supervised 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