Lec. |
Date |
Topic |
Lecture Material and Readings |
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 |
Slides Project 1 instructions and sample file available Tutorial 1 (in class) |
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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 |
Slides | ||

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 | ||

Decision trees |
Suggested readings: Sec.14.4 of Bishop. Sec.9.2 of Hastie et al. |
SlidesProject 1 due.Tutorial 2: Thursday Sept.28, TR3120, 4-5pm. |
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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 | ||

Feature construction and selection |
Slides Project 2 instructions now available |
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Thanksgiving (no class) |
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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 | ||

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 | ||

Support vector machines (cont'd) |
Suggested readings: See lecture 9. |
Slides | ||

Unsupervised learning |
Slides Project 2 due. |
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Neural networks |
Suggested readings Ch.11 of Hastie et al. Ch.5 of Bishop Ch.14 of Shalev-Schwartz |
Slides Project 3 instructions now available Tutorial 3, TR3120, 6-7pm |
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Neural networks (cont'd) |
Slides | |||

Deep learning |
Slides | |||

Deep learning (cont'd) |
Slides | |||

Semi-supervised learning / Generative Models |
Slides | |||

Bayesian Inference |
Suggested reading Bishop 1.2.3, 1.2.6, 2.3.6 Possibly Shalev-Scharz 24.5, Hastie 8.3 |
Slides Project 3 due. |
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Gaussian Processes |
Suggested reading: Bishop 3.3, 6.4.1, 6.4.2 Rasmussen & Williams, chapter 2 |
Slides Project 4 instructions now available |
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Bayesian Optimization |
Suggested reading: Bishop 6.4.3, 6.4.4 Rasmussen & Williams, chapter 5.1-5.4 Snoek & Larochelle, paper |
SlidesTutorial 4, TR3120, 7-9pm (2 * 1-hour sessions) |
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No class. Midterm (confirmed, 6-8pm, in Leacock 132). |
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Parallelization for large-scale ML |
Slides | |||

Missing data |
Slides | |||

Final project presentation session with TAs |
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Final project presentation session with TAs |
Project 4 report due (can be submitted until Dec.15 without late penalty). |