The following is a preliminary schedule of the lectures and presentations for CS 4033/5033 Machine Learning for Fall 2016. 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: Introduction |
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| Aug 22 | What is machine learning? What will I learn if I take this class? Types of machine learning. Overview of the course. | Elements of Statistical Learning (ESL), Chapter 1 | Pretest (in class) | |
Aug 24
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ML Introduction, Project introduction, Machine Learning that matters | ML that Matters paper | Project, HW 1 | |
Week 2: Project and Nearest Neighbor and Regression methods |
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| Aug 29 | Introduction to SL, Least Squares, Linear Regression, Nearest Neighbor, K-means clustering |
ESL Chapter 2.1-2.3, ESL Chapter 3.1-3.2 | HW 1 | |
Aug 31
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Oral project proposals | Oral project proposals | ||
Week 3: Regression methods |
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| Sep 5 | Labor Day (no class)
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| Sep 7 | Nearest neighbor, K-means clustering Ridge, Lasso, and Elastic nets Logistic regression Start of overfitting |
Ridge/Lasso/Elastic: ESL Chapter 3.3-3.8 Logistic: Wikipedia and ESL Chapter 4.4
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HW 2 | Written project proposal, teams formed |
Week 4: Neural nets and Deep learning |
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| Sep 12 | Overfitting, Bias-Variance Tradeoff, Model selection, Model evaluation, Introduction to Neural Networks |
ESL Chapter 7 |
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| Sep 14 | Neural Networks, Training the network |
ESL Chapter 11, Mitchell Ch 4 | HW 2 (due Sep 16) | |
Week 5: Neural nets and Deep learning |
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| Sep 19 | Training the network/Backpropagation, examples |
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Checkpoint 1 |
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| Sep 21 | Neural net active learning/In-class homework exercise | HW 3 | HW 3 | |
Week 6: Model evaluation |
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Sep 26 | Convolutional Neural Nets/Deep learning |
Neural Networks and Deep Learning chapter 6 and see the readings on Canvas |
HW 4 |
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| Sep 28 | Convolutional Neural Nets/Deep learning |
Checkpoint 2 | ||
Week 7: Tree-based methods |
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Oct 3 |
Decision trees |
ESL Chapter 9.2, Mitchell book chapter 3 |
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Oct 5 |
Boosting, Bagging, Ensemble methods, Random Forests and Gradient Boosted Regression Trees
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ESL Chapter 8.7, ESL Chapter 10, ESL Chapter 15 | HW 4 |
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Week 8: Reinforcment Learning |
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| Oct 10 | Guest lecture: Dan Sheldon (Departmental colloquium) | HW 5 | Checkpoint 3 | |
| Oct 12 | Boosting, Bagging, Ensemble methods, Random Forests and Gradient Boosted Regression Trees, Introduction to Reinforcement learning |
Sutton & Barto: Chapter 1 | ||
Week 9: Reinforcment Learning |
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Oct 17 |
Exploration & Exploitation, The RL Problem |
Sutton & Barto: 2.1-2.3, 2.5-2.7 |
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| Oct 19 | The RL Problem and Temporal Difference learning |
Sutton & Barto: Chapter 3 |
HW 5 (Oct 21) | |
Week 10: Project reports |
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Oct 24 |
Oral reports: Checkpoint 4 |
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Checkpoint 4 | |
Oct 26 |
Oral reports: Checkpoint 4 |
Checkpoint 4 |
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Week 11: Reinforcement Learning |
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| Oct 31 | The RL Problem and Temporal Difference learning |
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Nov 2 |
MDP homework in class | HW 6 | HW 6 | |
Week 12: Support Vector Machines and Kernel Methods |
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| Nov 7 | TD learning, Eligibility Traces, RL with Options, Advanced RL as needed for projects |
Sutton & Barto: Chapter 6-7 | Checkpoint 5 |
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| Nov 9 | Advanced RL as needed for projects, Advanced topics in neural nets including function approximation with RL | RL/NN (see RL Book 11.1) and ML book 11.9-11.13 | HW 7 | |
Week 13: Graphical Models |
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| Nov 14 | Kernel methods, Support Vector Machines, Kernel trick, SVR, SVMs | ESL Chapter 11 |
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| Nov 16 | SVMs continued |
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Checkpoint 6 | |
Week 14: Graphical Models |
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| Nov 21 | Introduction to Bayesian Networks | Mitchell NEW Chapter 3 (old chapter 6 but it is updated) | HW 7 | |
| Nov 23 | Thanksgiving vacation |
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Week 15: EM and graphical models |
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| Nov 28 | Inference, Naive Bayes |
HW 8 | Preliminary writeup | |
| Nov 30 | Using Bayes Nets for Inference, Conditional Independence, Inference |
Using Bayes Nets for Inference, Conditional Independence, Inference | HW 8 | |
Week 16: Future of ML |
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| Dec 5 | Finish Bayes, Project summaries |
Peer reviews | ||
| Dec 7 | Project summaries | |||
Week 17: Final |
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| Dec 12, 4:30-6:30 | Final writeup due |
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