The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2008. 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 26 (Week 1) | What is machine learning? What will I learn if I take this class? | Pretest | Pretest | |
| Aug 28 | Function approximation, Project introduction | Introduction (Ch 1, Mitchell) | Project | |
| Sep 2 (Week 2) | Oral project proposals |
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| Sep 4 | Reinforcement Learning, Exploration | Chapter 1 (Sutton & Barto) | ||
| Sep 9 (Week 3) | Exploration, The RL problem | 2.1-2.3, 2.5-2.7 (Sutton & Barto) |
Written proposal | |
| Sep 11 | The RL problem | Chapter 3 (Sutton & Barto) | HW 1 | |
| Sep 16 (Week 4) | The RL problem | |||
| Sep 18 | The RL problem | Checkpoint 1 | ||
| Sep 23 (Week 5) | Dynamic Programming | Chapter 4 (Sutton & Barto) |
HW 1 | |
| Sep 25 | Temporal Difference learning | 6.1-6.5, 6.8 (Sutton & Barto) | HW 2 | HW 2 group |
| Sep 30 (Week 6) | Advanced RL, Neural Networks: what they are | Chapter 4 (Mitchell) or Section 20.5 in Russell & Norvig) | Checkpoint 2 | |
| Oct 2 | Training the network, Backprop | |||
| Oct 7 (Week 7) | Backprop |
HW 2 | ||
| Oct 9 | Advanced topics in neural nets, Oral update: Battleship | RL/NN (see RL Book 11.1) | Checkpoint 3 | |
| Oct 14 (Week 8) | Overfitting/Model Complexity |
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| Oct 16 | Review of probability | Ch 13 (Russell & Norvig) | HW 3 | |
Oct 21 (Week 9) |
Probability review (finish) Oral update: Self-managed Systems I and II, Football | Checkpoint 4 | ||
| Oct 23 | Linear regression, Gaussians,Naive Bayes | Ch 8 (Mitchell sample chapter) |
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| Oct 28 (Week 10) | Graphical models: Bayesian Networks | HW 3 | ||
| Oct 30 | Bayesian Networks, Oral update: Robots, Captcha, Mesonet, Precipitation, Demos to Dr. McGovern: Self-managed Systems I and II, Football, Battleship | Chapter 14 ( Russell & Norvig) or Chapter 6 (Mitchell) | Checkpoint 5 | |
| Nov 4 (Week 11) | Bayesian networks | HW 4 | ||
| Nov 6 | Conditional Independence |
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| Nov 11 (Week 12) | Exact inference, Demos to Dr McGovern: Robots, Captcha, Mesonet, Precipitation | Checkpoint 6 | ||
| Nov 13 | Clustering, Mixture models, Expectation Maximization | Section 20.3 (Russell & Norvig) or Ch 6 (Mitchell) | HW 5 | HW 4 |
| Nov 18 (Week 13) | EM continued | |||
| Nov 20 | EM for Bayesian Networks (learning structure) | |||
| Nov 25 (Week 14) | IC Algorithm | Preliminary writeup | ||
| Nov 27 | Thanksgiving vacation |
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| Dec 2 (Week 15) | IC Algorithm, Support Vector Machines/Kernel Machines | Section 20.6 (Russell & Norvig) | Peer reviews | |
| Dec 4 | wrapup and schedule catch up | HW 5 | ||
| Dec 9 (Week 16) | Project presentations: Captcha, Precipitation |
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| Dec 11 | Project presentations: Patrick, Mesonet |
Final writeup due | ||
| December 12, 2-5pm | Poster presentation: Sarkeys A & B |
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| Dec 18 (Final exam period 1:30-3:30) | Project presentations: Robot, football, Battleship, Hira |
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