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 |
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| 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 |
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| Sep 29 | Logistic regression, Introduction to Neural Networks |
Readings on D2L | Homework 3 | |
| Oct 4 (Week 7) | Training the network, Backprop |
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| Oct 6 | Backprop | Checkpoint 3: group meetings with Dr McGovern
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| Oct 11 (Week 8) | Backprop - in class homework on neural nets
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Homework 4 | ||
| Oct 13 | Class cancelled |
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Oct 18 (Week 9) General techniques |
Oral status reports: checkpoint 4 |
Checkpoint 4 |
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| 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,
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Readings on D2L |
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Oct 27 SVMs
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Boosting, Bagging, Ensemble methods |
Checkpoint 5
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| 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
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| 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 |
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| Nov 22 (Week 14) | Naive Bayes | Preliminary writeup, Homework 6 | ||
| Nov 24 | Thanksgiving vacation |
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| 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) |
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| Dec 8 | Hybrid networks | Homework 7 | ||
| December 9, 2-5pm | CS poster session: Devon Hall atrium |
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| Dec 16, 1:30-3:30 | Final writeup due |
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