CS 4033/5033 Machine Learning Class schedule

Fall 2011

The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2011. 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 Due
Aug 23 (Week 1) What is machine learning? What will I learn if I take this class? Types of machine learning.      

Aug 25

Reinforcement Learning

Project introduction, Introduction to RL Chapter 1 (Sutton & Barto) Project Pretest
Aug 30 (Week 2)
Oral project proposals
Sep 1 Intro to RL, Exploration & Exploitation 2.1-2.3, 2.5-2.7 (Sutton & Barto)    
Sep 6 (Week 3) The RL problem

Chapter 3 (Sutton & Barto)

Homework 1 Written project proposal, teams formed
Sep 8 The RL problem      
Sep 13 (Week 4) The RL problem   Homework 2 Homework 1
Sep 15 Dynamic Programming Chapter 4 (Sutton & Barto)    
Sep 20 (Week 5) Temporal Difference learning

6.1-6.5, 6.8 (Sutton & Barto)

  Checkpoint 1
Sep 22 Eligibility Traces, RL with Options Ch 7 (Sutton & Barto) Homework 3 Homework 2

Sep 27 (Week 6)

Supervised Learning

Nearest neighbor methods, Regression, Locally weighted regression Ch 7 (sections 7.1-7.2 except * sections, 7.4 except *) from Murphy book  

Checkpoint 2

Sep 29

Logistic regression, Introduction to Neural Networks

Readings on D2L Homework 3
Oct 4 (Week 7)

Training the network, Backprop

Oct 6 Backprop    

Checkpoint 3: group meetings with Dr McGovern


Oct 11 (Week 8)

Backprop - in class homework on neural nets


    Homework 4
Oct 13

Class cancelled


Oct 18 (Week 9)

General techniques

Oral status reports: checkpoint 4

Checkpoint 4

Oct 20 Finish oral status report (checkpoint 4), Advanced topics in neural nets including function approximation with RL RL/NN (see RL Book 11.1), also 8.1-8.4 in RL book    

Oct 25 (Week 10)

Ensemble methods

Function approximation with RL,


Readings on D2L


Oct 27



Boosting, Bagging, Ensemble methods


Checkpoint 5


Nov 1 (Week 11)

Overfitting, Introduction to Support Vector Machines

SVM paper (D2L) Homework 5  

Nov 3

Graphical Models

Support vector machines, kernels    
Nov 8 (Week 12) Support Vector Regression SVR paper (D2L)  

Checkpoint 6



Nov 10 Introduction to Bayesian Networks Chapter readings on D2L Homework 6 Homework 5
Nov 15 (Week 13) Using Bayes Nets for Inference    
Nov 17 Conditional Independence, Inference


Nov 22 (Week 14) Naive Bayes     Preliminary writeup, Homework 6
Nov 24
Thanksgiving vacation
Nov 29 (Week 15) Class cancelled Homework 7 Peer reviews
Dec 1 Mixture models, Expectation Maximization (EM)      
Dec 6 (Week 16)
EM for Bayesian Networks (learning structure)
Dec 8 Hybrid networks     Homework 7
December 9, 2-5pm
CS poster session: Devon Hall atrium
Dec 16, 1:30-3:30
Final writeup due