Lec. |
Date |
Topic |
Lecture Material and Readings |
Slides and Projects |

Introduction to machine learning. |
Mandatory reading: This paper. Suggested readings: Bishop, Ch.1-2. Hastie et al., Ch.1. Shalev-Schwartz et al., Ch.2. |
Slides | ||

Linear regression. |
Suggested readings: Bishop, Ch.3. Hastie et al., Ch.2 (Sec.2.1-2.4, 2.9). Shalev-Schwartz et al., Ch.9 |
Slides | ||

Linear regression. |
Suggested readings: Ch.3 (Sec.3.1-3.4, 3.9) of Hastie et al. Ch.3 of Bishop (Sec.3.1-3.2). Ch.5 and 11 of Shalev-Schwartz |
Mini-project 1 will be released this week
Slides |
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Linear classification. |
Suggested readings: Ch.4 of Hastie et al. Ch.4 of Bishop (Sec.4.1-4.3). Sec.9.3 of Shalev-Schwartz |
First tutorial this Friday January 19th, 6-7 pm, Stewart Biology S3/3!
Slides Project 1 instructions Data project 1 | ||

Linear classification. |
Suggested readings: Sec. 6.6.3 of Hastie et al. Ch.4 of Bishop (Sec.4.1-4.3). Sec.24.1-24.3 Shalev-Schwartz |
Slides | ||

Performance analysis and error estimation. |
Suggested readings: Ch.7 of Hastie et al. Wagstaff (2012) paper |
Slides Mini-project 1 due on Jan. Mini-project 2 released on Jan |
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Instance-based learning |
Suggested readings: Sec.2.5 of Bishop. Sec.13.3 of Hastie et al. Ch.19 of Shalev-Schwartz |
Slides | ||

Support vector machines |
Suggested readings: Ch.7 of Bishop. Ch.12 (Sec.12.1-12.4) of Hastie et al. Ch.15 of Shalev-Schwartz For more on convex optimization: see book by S. Boyd and L. Vandenberghe |
Slides Project 2 instructions Project 2 data Second tutorial this Friday February 2nd, 6-7 pm, Stewart Biology S3/3! Notebook |
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Support vector machines (cont'd) |
Suggested readings: See lecture 9. |
Slides | ||

Decision trees |
Suggested readings: Sec.14.4 of Bishop. Sec.9.2 of Hastie et al. |
Mini-project 2 due Feb. Slides |
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Ensemble methods |
Suggested readings: Sec.8.7, Ch.10 of Hastie et al. Ch.14 of Bishop Ch.10 of Shalev-Schwartz |
Slides Mini-project 3 instructions Mini-project 3 data |
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Ensemble learning, Feature design |
Suggested readings: Ch. 3.1 of Bishop Ch. 8.8 of Hastie | Slides |
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Feature construction and selection |
Suggested readings: Ch. 12 of Bishop Ch. 14 of Hastie Ch. 23 of Shalev-Schwartz |
Mini-project 3 due Feb. Slides | ||

Neural networks |
Suggested readings Ch.11 of Hastie et al. Ch.5 of Bishop Ch.14 of Shalev-Schwartz Ch.6 of Goodfellow et al. |
Slides | ||

Neural networks (cont'd) |
Suggested readings: See lecture 14 |
Slides Project 4 instructions Project 4 Kaggle | ||

Deep learning |
Suggested readings: Ch.9 of Goodfellow et al. |
3rd tutorial (PyTorch) on March 2nd Slides | ||

Study week - no class |
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Study week - no class |
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Deep learning (cont'd) |
Suggested readings: Ch. 10 of Goodfellow et al. |
Slides | ||

Bayesian Inference |
Suggested reading Bishop 1.2.3, 1.2.6, 2.3.6 Possibly Shalev-Scharz 24.5, Hastie 8.3 |
Slides | ||

Bayesian Linear Regression and Bayesian Optimization |
Suggested reading: Bishop 3.3, 6.4.1, 6.4.2 Rasmussen & Williams, chapter 2 |
Slides | ||

Gaussian Processes |
Suggested reading: Bishop 6.4.1, 6.4.2 Rasmussen & Williams, chapter 2 |
Project 4 due March 21st Slides | ||

Unsupervised learning: Clustering |
Suggested readings Ch. 9 of Bishop Ch. 14 of Hastier Ch.22. of Shalev-Schwartz |
4th tutorial (Midterm Q&A) March 27th | ||

Unsupervised learning: Clustering |
Suggested readings Ch. 9 of Bishop Ch. 14 of Hastier Ch.22. of Shalev-Schwartz |
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Easter Monday - No class. |
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No class. Midterm 5:30-8:30 pm (TBC) . |
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Introduction to Reinforcement Learning |
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Frontiers in machine learning |
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Final project presentation session, 1800-1930 |
Stewart Biology N2/2 (TBC) | |||

Final project presentation session |
Final project due Apr. 20th | |||