The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2012. This specific order of each topic will depend on the class projects and interest. This will be finalized in the first week. The schedule will be updated throughout the semester as we delve deeper into special topics and/or add additional topics of interest. The project deadlines are in red and will not change, even if other parts of the schedule change. Major new topics are noted in blue.
Date  Topic  Assigned Reading  Assigned today  Due today 
Aug 21 (Week 1)  What is machine learning? What will I learn if I take this class? Types of machine learning.  Chapter 1 (ML book)  Pretest (in class)  
Aug 23

Project introduction, Machine Learning that matters  ML that Matters paper  Project, HW 1  
Aug 28 (Week 2)  Oral project proposals 
Oral project proposals  
Aug 30 Reinforcement learning 
Introduction to RL  Chapter 1 (Sutton & Barto)  HW 1  
Sep 4(Week 3)  Exploration & Exploitation, The RL Problem  2.12.3, 2.52.7 (Sutton & Barto) Chapter 3 (Sutton & Barto) 
HW 2  Written project proposal, teams formed 
Sep 6  Dr McGovern was sick  
Sep 11 (Week 4)  The RL problem  HW 2  
Sep 13  The RL problem  
Sep 18 (Week 5)  Dynamic Programming 
Chapter 4 (Sutton & Barto) 
Checkpoint 1  
Sep 20  Homework 3 in class  HW 3  HW 3  
Sep 25 (Week 6)  Temporal Difference learning 
6.16.5, 6.8 (Sutton & Barto) 
Checkpoint 2 

Sep 27  Eligibility Traces, RL with Options 
Ch 7 (Sutton & Barto) 

Oct 2 (Week 7) Supervised learning 
Introduction to supervised learning, Regression, Logistic regression 
Chapter 2 (regression in 2.6), Chapter 10 for logistic regression  
Oct 4 Graphical models 
Introduction to Bayesian Networks  Chapter 16.1  Checkpoint 3


Oct 9 (Week 8)  Homework 4 in class

HW 4  HW 4  
Oct 11  Using Bayes Nets for Inference 

Oct 16 (Week 9) 
Conditional Independence, Inference  Chapter 16.2, 16.4 
HW 5 

Oct 18  Oral reports: Checkpoint 4  Checkpoint 4  
Oct 23 (Week 10) 
Oral reports finishing, Homework discussion


HW 5  
Oct 25 
Naive Bayes, Mixture models, Expectation Maximization (EM) 
Chapter 16.3, 7.4  HW 6  Checkpoint 5 
Oct 30 (Week 11)  Finish EM, EM for Bayesian Networks (learning structure) 
16.7  
Nov 1 
Introduction to Neural Networks  Chapter 11 up to 11.6  HW 7  HW 6 
Nov 6 (Week 12)  Training the network, Backprop  11.711.8  Checkpoint 6 

Nov 8  Advanced topics in neural nets including function approximation with RL and advanced network configurations  RL/NN (see RL Book 11.1) and ML book 11.911.13  
Nov 13 (Week 13)  Boosting, Bagging, Ensemble methods  Chapter 17 (ML book) 
HW7  
Nov 15  Overfitting, Model selection  Chapter 2 (ML book) 

Nov 20 (Week 14)  PCA/Dimensionality reduction  Chapter 6  Preliminary writeup  
Nov 22  Thanksgiving vacation 

Nov 27 (Week 15)  Neural net active learning/Homework 8 exercise 
HW 8  Peer reviews  
Nov 29  Multiple instance learning, realworld applications  HW 8  
Dec 4 (Week 16)  Kernel methods, Support Vector Machines, Kernel trick, SVR 
Chapter 13 up to 13.4  
Dec 6  Review/Catch up  
December 7, 25pm  CS poster session: Devon Hall atrium 

Dec 11, 1:303:30  Final writeup due 