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 |
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Decision trees |
Suggested readings: Sec.14.4 of Bishop. Sec.9.2 of Hastie et al. |
Project 1 due. | ||

Instance-based learning |
Suggested readings: Sec.2.5 of Bishop. Sec.13.3 of Hastie et al. Ch.19 of Shalev-Schwartz |
Project 2 availableTutorial 2: Thursday Sept.28, TR3120, 4-5pm. |
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Feature construction and selection |
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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 |
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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 |
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Support vector machines (cont'd) |
Suggested readings: See lecture 9. |
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Unsupervised learning |
Project 2 due. | |||

Neural networks |
Suggested readings Ch.11 of Hastie et al. Ch.5 of Bishop Ch.14 of Shalev-Schwartz |
Project 3 available.Tutorial 3 |
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Neural networks (cont'd) |
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Deep learning |
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Deep learning (cont'd) |
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Semi-supervised learning / Generative Models |
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Bayesian Inference |
Project 3 due. |
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Gaussian Processes |
Project 4 available. | |||

Bayesian Optimization |
Tutorial 4 |
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Midterm (confirmed, 6-8pm, in Leacock 132). |
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Parallelization for large-scale ML |
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Missing data |
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Final project discussion session with TAs (optional) |
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Final project presentation session |
Project 4 report due. |