The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2012. 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 today | Due today |
| Aug 21 (Week 1) | What is machine learning? What will I learn if I take this class? Types of machine learning. | Chapter 1 (ML book) | Pretest (in class) | |
Aug 23
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Project introduction, Machine Learning that matters | ML that Matters paper | Project, HW 1 | |
| Aug 28 (Week 2) | Oral project proposals |
Oral project proposals | ||
Aug 30 Reinforcement learning |
Introduction to RL | Chapter 1 (Sutton & Barto) | HW 1 | |
| Sep 4(Week 3) | Exploration & Exploitation, The RL Problem | 2.1-2.3, 2.5-2.7 (Sutton & Barto) Chapter 3 (Sutton & Barto) |
HW 2 | Written project proposal, teams formed |
| Sep 6 | Dr McGovern was sick | |||
| Sep 11 (Week 4) | The RL problem | HW 2 | ||
| Sep 13 | The RL problem | |||
| Sep 18 (Week 5) | Dynamic Programming |
Chapter 4 (Sutton & Barto) |
Checkpoint 1 | |
| Sep 20 | Homework 3 in class | HW 3 | HW 3 | |
Sep 25 (Week 6) | Temporal Difference learning |
6.1-6.5, 6.8 (Sutton & Barto) |
Checkpoint 2 |
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| Sep 27 | Eligibility Traces, RL with Options |
Ch 7 (Sutton & Barto) |
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Oct 2 (Week 7) Supervised learning |
Introduction to supervised learning, Regression, Logistic regression |
Chapter 2 (regression in 2.6), Chapter 10 for logistic regression | ||
Oct 4 Graphical models |
Introduction to Bayesian Networks | Chapter 16.1 | Checkpoint 3
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| Oct 9 (Week 8) | Homework 4 in class
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HW 4 | HW 4 | |
| Oct 11 | Using Bayes Nets for Inference |
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Oct 16 (Week 9) |
Conditional Independence, Inference | Chapter 16.2, 16.4 |
HW 5 |
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| Oct 18 | Oral reports: Checkpoint 4 | Checkpoint 4 | ||
Oct 23 (Week 10) |
Oral reports finishing, Homework discussion
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HW 5 | |
Oct 25 |
Naive Bayes, Mixture models, Expectation Maximization (EM) |
Chapter 16.3, 7.4 | HW 6 | Checkpoint 5 |
| Oct 30 (Week 11) | Finish EM, EM for Bayesian Networks (learning structure) |
16.7 | ||
Nov 1 |
Introduction to Neural Networks | Chapter 11 up to 11.6 | HW 7 | HW 6 |
| Nov 6 (Week 12) | Training the network, Backprop | 11.7-11.8 | Checkpoint 6 |
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| Nov 8 | Advanced topics in neural nets including function approximation with RL and advanced network configurations | RL/NN (see RL Book 11.1) and ML book 11.9-11.13 | ||
| Nov 13 (Week 13) | Boosting, Bagging, Ensemble methods | Chapter 17 (ML book) |
HW7 | |
| Nov 15 | Overfitting, Model selection | Chapter 2 (ML book) |
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| Nov 20 (Week 14) | PCA/Dimensionality reduction | Chapter 6 | Preliminary writeup | |
| Nov 22 | Thanksgiving vacation |
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| Nov 27 (Week 15) | Neural net active learning/Homework 8 exercise |
HW 8 | Peer reviews | |
| Nov 29 | Multiple instance learning, real-world applications | HW 8 | ||
| Dec 4 (Week 16) | Kernel methods, Support Vector Machines, Kernel trick, SVR |
Chapter 13 up to 13.4 | ||
| Dec 6 | Review/Catch up | |||
| December 7, 2-5pm | CS poster session: Devon Hall atrium |
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| Dec 11, 1:30-3:30 | Final writeup due |
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