The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2015. 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.
Date  Topic  Assigned Reading  Assigned today  Due today 
Week 1 

Aug 24  What is machine learning? What will I learn if I take this class? Types of machine learning.  Mitchell Chapter 1  Pretest (in class)  
Aug 26

ML Introduction, Project introduction, Machine Learning that matters  ML that Matters paper  Project, HW 1  
Week 2 

Aug 31  Reinforcement learning 
Chapter 1 (Sutton & Barto)  HW 1  
Sep 2

Oral project proposals  Oral project proposals  
Week 3 

Sep 7  Labor Day (no class)


Sep 9  Exploration & Exploitation, The RL Problem  2.12.3, 2.52.7 (Sutton & Barto) Chapter 3 (Sutton & Barto) 
Written project proposal, teams formed  
Week 4 

Sep 14  Class cancelled  
Sep 16  The RL problem  HW 2  
Week 5 

Sep 21  The RL problem 

Checkpoint 1  
Sep 23  Temporal Difference learning  HW 2  
Week 6 

Sep 28  MDP homework (3) in class 
6.16.5, 6.8 (Sutton & Barto) 
HW 3  HW 3 
Sep 30  TD learning, Eligibility Traces, RL with Options, Advanced RL as needed for projects 
Ch 7 (Sutton & Barto)  Checkpoint 2  
Week 7 

Oct 5 
Introduction to supervised learning, Regression, Logistic regression, Introduction to Neural Networks 
Mitchell Chapter 4  HW 4  
Oct 7 
Training the network, Backprop 


Week 8 

Oct 12  Advanced topics in neural nets including function approximation with RL and advanced network configurations, self organizing maps 
RL/NN (see RL Book 11.1) and ML book 11.911.13  Checkpoint 3  
Oct 14  Neural net active learning/Homework exercise 
HW 5  HW 4, HW 5  
Week 9 

Oct 19 
Deep learning and convolution networks 
Papers on D2L 


Oct 21  Decision trees  Mitchell book chapter 3 The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: pages 305317 (Section 9.2). 

Week 10 

Oct 26 
Oral reports finished


Checkpoint 4  
Oct 28 
Oral reports: Checkpoint 4 
HW6 


Week 11 

Nov 2  Kernel methods, Support Vector Machines, Kernel trick, SVR 
Papers on D2L  
Nov 4 
Class cancelled  Checkpoint 5  
Week 12 

Nov 9  SVMs  HW 6 

Nov 11  Overfitting, Model selection, Model evaluation, Boosting, Bagging, Ensemble methods  HW 7  
Week 13 

Nov 16  Random Forests, Gradient Boosted Regression Trees  Papers on D2L 
Checkpoint 6 (accepted until Nov 18)  
Nov 18  Introduction to Bayesian Networks  Mitchell NEW Chapter 3 (old chapter 6 but it is updated) 
HW 7  
Week 14 

Nov 23  Inference, Naive Bayes  HW 8  
Nov 25  Thanksgiving vacation 

Week 15 

Nov 30  Using Bayes Nets for Inference, Conditional Independence, Inference 
Preliminary writeup  
Dec 2  Kmeans, Mixture models, Expectation Maximization (EM) 
Mitchell Chapter 8  
Week 16 

Dec 7  EM for Bayesian Networks (learning structure) 
HW 8, Peer reviews  
Dec 10  Future/Applications  
Week 17 

December 11 25pm  CS poster session: Devon Hall atrium 

Dec 17, 4:306:30  Final writeup due 