The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2007. 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. All readings are from Pattern Recognition and Machine Learning by Christopher Bishop unless otherwise noted. 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 21 (Week 1) | Introduction: why am I taking this class anyway? | Pretest | ||
| Aug 23 | Function approximation, Project introduction | Chapter 1, section 1 | Project | Pretest |
| Aug 28 (Week 2) | Reinforcement Learning, Exploration | Chapter 1 (Sutton & Barto) | ||
| Aug 30 | Exploration, The RL problem | 2.1-2.3, 2.5-2.7 (Sutton & Barto) |
HW 1 | Written proposal |
| Sep 4 (Week 3) | Oral project proposals |
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| Sep 6 | The RL problem | Chapter 3 (Sutton & Barto) | ||
| Sep 11 (Week 4) | The RL problem | |||
| Sep 13 | Dynamic Programming | Chapter 4 (Sutton & Barto) | HW 2 | HW1, Checkpoint 1 |
| Sep 18 (Week 5) | Temporal Difference learning | 6.1-6.5, 6.8 (Sutton & Barto) |
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| Sep 20 | Advanced RL topics | |||
| Sep 25 (Week 6) | Probability Theory | Chapter 1, section 2 | Checkpoint 2 | |
| Sep 27 | Probability theory | HW 2 | ||
| Oct 2 (Week 7) | Linear Regression | Chapter 3, section 1 | HW 3 | |
| Oct 4 | Classification: Discriminant Function | Chapter 4: sections 4.1-4.13, 4.17, 4.3.2 | Checkpoint 3 | |
| Oct 9 (Week 8) | Classifications: laptop exercise |
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| Oct 11 | Neural Networks: what they are | Chapter 5, section 1 |
HW 3 | |
| Oct 16 (Week 9) | Training the network, Backprop | 5.2.1, 5.2.4 | Checkpoint 4 | |
| Oct 18 | Backprop |
Chapter 5, section 3 through 5.3.2 |
HW 4 | |
| Oct 23 (Week 10) | Advanced topics in neural nets | RL/NN (see RL Book 11.1) | ||
| Oct 25 | Overfitting/Model Complexity Oral progress updates: Teams Robot, Poker, Netflix, Tetris |
Checkpoint 5: Teams STWX, STEM, TV | ||
| Oct 30 (Week 11) | Graphical models: Bayesian Networks | Chapter 8, section 1 | HW 4 | |
| Nov 1 | Bayesian networks | |||
| Nov 6 (Week 12) | Conditional Independence Oral progress update: Team STWX |
Chapter 8, section 2 | HW 5 | Checkpoint 6: Teams Robot, Poker, Netflix, Tetris |
| Nov 8 | Exact inference | Chapter 8, section 4.6 | ||
| Nov 13 (Week 13) | Mixture models, Expectation Maximization | Chapter 9, section 1 and 2 | ||
| Nov 15 | EM continued | HW 5 | ||
| Nov 20 (Week 14) | EM for Bayesian Networks (learning structure) | HW 6 | Preliminary writeup | |
| Nov 22 | Thanksgiving vacation (enjoy your turkey!) |
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| Nov 27 (Week 15) | Bayes Models appplied to the real world | Peer reviews | ||
| Nov 29 | ML applied to the real world | HW 6 due 5pm Friday Nov 30 | ||
| Dec 4 (Week 16) | Class cancelled |
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| Dec 6 | Project presentations: Teams Teris, MythTV, STEM |
Final writeup accepted until 5pm, Dec 7 | ||
| December 7, 2-5pm | Poster presentation: Sarkeys A & B |
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| Dec 11 (Final exam period 1:30-3:30) | Project presentations: Teams SteWx, Poker, Netflix, Robot |
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