Notes:
- This schedule is a rough approximation and subject to change.
- Reading assignments are from A Course in Machine Learning, by Hal Daumé III.
- Homework is due on Canvas by 11:59pm MT on Fridays.
| Week | Date | Reading | Topic | Slides | Homework |
|---|---|---|---|---|---|
| 1 | 8/24 | Machine Learning Intro. | slides | ||
| 8/26 | Chapter 1 | Decision Trees Part 1 | slides | ||
| 8/28 | Decisions Trees Part 2 | slides | |||
| 2 | 8/31 | Chapter 2 | Limits of Learning/Bias-Variance Trade-off | slides | |
| 9/2 | Chapter 3 | K-Nearest Neighbors | slides | ||
| 9/4 | Chapter 4 | Perceptron | slides | ||
| 3 | 9/7 | Labor day | |||
| 9/9 | Hands on decision KNN and perceptron | Notebooks | |||
| 9/11 | Chapter 5 | Feature Engineering | slides | HW1 due | |
| 4 | 9/14 | Chapter 9 & Chapter 7 | Logistic regression | slides | |
| 9/16 | Chapter 7 | Stochastic gradient part 1 | slides | ||
| 9/18 | Chapter 14 | Stochastic gradient part 2 | slides/Notebooks | ||
| 5 | 9/21 | Chapter 5 | Hands on Stochastic gradient | Notebooks | |
| 9/23 | Chapter 5 | Validation & Evaluation metrics | slides | ||
| 9/25 | Chapter 6 | Multiclass classification | slides | HW2 due | |
| 6 | 9/28 | Hands on validation and evaluation metrics | Notebooks | ||
| 9/30 | Prelim 1 (week 1, 2, 3, 4) | ||||
| 10/2 | Chapter 10 | Neural Nets and MLP | slides | ||
| 7 | 10/5 | Chapter 10 | Neural network architecture | slides | |
| 10/7 | Chapter 10 | Back Propagation Part 1 | slides | ||
| 10/9 | Chapter 10 | Back Propagation Part 2 | slides | Final project team formation due | |
| 8 | 10/12 | Hands on Neural nets | Notebooks | ||
| 10/14 | Chapter 7 | Hard-Margin SVM | slides | ||
| 10/16 | Soft-Margin SVM Part 1 | slides | HW3 due | ||
| 9 | 10/19 | Additional reading | Soft-Margin SVM Part 2 | slides | |
| 10/21 | Chapter 11 | Kernel trick | slides | ||
| 10/23 | Hands on SVM | Notebooks | Final project proposal due | ||
| 10 | 10/26 | Hands on SVM continued | Notebooks | ||
| 10/28 | Bagging and Random Forests | slides | |||
| 10/30 | Boosted Decision Trees | slides | HW4 due | ||
| 11 | 11/2 | Hands-On AdaBoost | Notebooks | ||
| 11/4 | Review | slides | |||
| 11/6 | Prelim 2 (Week 5, 6, 7, 8) | ||||
| 12 | 11/9 | Chapter 15 | Unsupervised learning/PCA | slides | |
| 11/11 | Clustering | slides | |||
| 11/13 | Chapter 16 | Gaussian mixture models and Expectation-minimization algorithms | slides | ||
| 13 | 11/16 | Topic models | slides | ||
| 11/18 | Project midpoint check-in | ||||
| 11/20 | Chapter 8 | Learning theory | slides | HW5 due | |
| 14 | 11/23 | Ethics and Machine Learning | slides | ||
| 11/25 | Enjoy Thanksgiving! | ||||
| 11/27 | Fall break | ||||
| 15 | 11/30 | Prelim 3 (Week 10, 11, 12, 13) | |||
| 12/2 | Hackathon I | ||||
| 12/4 | Hackathon II | ||||
| 16 | 12/7 | Final project presentation | Final project report due |