The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2015. 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.
| Date | Topic | Assigned Reading | Assigned today | Due today |
Week 1 |
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| Aug 24 | What is machine learning? What will I learn if I take this class? Types of machine learning. | Mitchell Chapter 1 | Pretest (in class) | |
Aug 26
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ML Introduction, Project introduction, Machine Learning that matters | ML that Matters paper | Project, HW 1 | |
Week 2 |
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| Aug 31 | Reinforcement learning |
Chapter 1 (Sutton & Barto) | HW 1 | |
Sep 2
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Oral project proposals | Oral project proposals | ||
Week 3 |
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| Sep 7 | Labor Day (no class)
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| Sep 9 | Exploration & Exploitation, The RL Problem | 2.1-2.3, 2.5-2.7 (Sutton & Barto) Chapter 3 (Sutton & Barto) |
Written project proposal, teams formed | |
Week 4 |
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| Sep 14 | Class cancelled | |||
| Sep 16 | The RL problem | HW 2 | ||
Week 5 |
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| Sep 21 | The RL problem |
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Checkpoint 1 | |
| Sep 23 | Temporal Difference learning | HW 2 | ||
Week 6 |
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Sep 28 | MDP homework (3) in class |
6.1-6.5, 6.8 (Sutton & Barto) |
HW 3 | HW 3 |
| Sep 30 | TD learning, Eligibility Traces, RL with Options, Advanced RL as needed for projects |
Ch 7 (Sutton & Barto) | Checkpoint 2 | |
Week 7 |
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Oct 5 |
Introduction to supervised learning, Regression, Logistic regression, Introduction to Neural Networks |
Mitchell Chapter 4 | HW 4 | |
Oct 7 |
Training the network, Backprop |
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Week 8 |
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| Oct 12 | Advanced topics in neural nets including function approximation with RL and advanced network configurations, self organizing maps |
RL/NN (see RL Book 11.1) and ML book 11.9-11.13 | Checkpoint 3 | |
| Oct 14 | Neural net active learning/Homework exercise |
HW 5 | HW 4, HW 5 | |
Week 9 |
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Oct 19 |
Deep learning and convolution networks |
Papers on D2L |
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| Oct 21 | Decision trees | Mitchell book chapter 3 The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman: pages 305-317 (Section 9.2). |
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Week 10 |
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Oct 26 |
Oral reports finished
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Checkpoint 4 | |
Oct 28 |
Oral reports: Checkpoint 4 |
HW6 |
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Week 11 |
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| Nov 2 | Kernel methods, Support Vector Machines, Kernel trick, SVR |
Papers on D2L | ||
Nov 4 |
Class cancelled | Checkpoint 5 | ||
Week 12 |
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| Nov 9 | SVMs | HW 6 |
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| Nov 11 | Overfitting, Model selection, Model evaluation, Boosting, Bagging, Ensemble methods | HW 7 | ||
Week 13 |
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| Nov 16 | Random Forests, Gradient Boosted Regression Trees | Papers on D2L |
Checkpoint 6 (accepted until Nov 18) | |
| Nov 18 | Introduction to Bayesian Networks | Mitchell NEW Chapter 3 (old chapter 6 but it is updated) |
HW 7 | |
Week 14 |
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| Nov 23 | Inference, Naive Bayes | HW 8 | ||
| Nov 25 | Thanksgiving vacation |
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Week 15 |
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| Nov 30 | Using Bayes Nets for Inference, Conditional Independence, Inference |
Preliminary writeup | ||
| Dec 2 | K-means, Mixture models, Expectation Maximization (EM) |
Mitchell Chapter 8 | ||
Week 16 |
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| Dec 7 | EM for Bayesian Networks (learning structure) |
HW 8, Peer reviews | ||
| Dec 10 | Future/Applications | |||
Week 17 |
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| December 11 2-5pm | CS poster session: Devon Hall atrium |
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| Dec 17, 4:30-6:30 | Final writeup due |
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