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Lec. |
Date |
Topic |
Lecture Material |
Homeworks and Readings |

Introduction to machine learning. |
Slides | Read this paper. Review basic notions of algebra and probabilities. Read Ch.1-2 of Bishop and Ch.1 of Hastie et al. |
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Linear regression. |
Slides | Suggested readings: Ch.2 (Sec.2.1-2.4, 2.9) of Hastie et al. Ch.3 of Bishop (Sec.3.1-3.2). |
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Linear regression. |
Slides Mini-project #1 available. |
Suggested readings: Ch.3 (Sec.3.1-3.4, 3.9) of Hastie et al. Ch.3 of Bishop (Sec.3.1-3.2). |
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Linear classification. |
Slides | Suggested readings: Ch.4 of Hastie et al. Ch.4 of Bishop (Sec.4.1-4.3). A paper by Ng & Jordan (NIPS, 2001). |
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Naive Bayes. |
Slides | Suggested readings: Sec. 6.6.3 of Hastie et al. Ch.13 (Sec.13.1-13.4) of the book Introduction to Information Retrieval. |
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Performance analysis and error estimation. |
Slides | Suggested readings: Ch.7 of Hastie et al. K. Wagstaff's (2012) paper |
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Practical session with python and scikit-learn. |
Mini-project #1 due. | |||

Decision trees |
Slides Mini-project #2 available. |
Suggested readings: Sec.14.4 of Bishop. Sec.9.2 of Hastie et al. |
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Instance-based learning |
Slides | Suggested readings: Sec.2.5 of Bishop. Sec.13.3 of Hastie et al. |
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Ensemble methods |
Slides | Suggested readings: Sec.8.7, Ch.10 of Hastie et al. Ch.14 of Bishop |
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Ensemble learning (cont'd) |
Slides | |||

Support vector machines |
Slides | Suggested readings: Sec.7.1 of Bishop. Ch.12 (Sec.12.1-12.4) of Hastie et al. For background on convex optimization: see this book by S. Boyd and L. Vandenberghe |
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Support vector machines (cont'd) |
Slides Mini-project #2 due. |
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Neural networks |
Slides Mini-project #3 available. |
Suggested readings Ch.11 of Hastie et al. Sec.5.1-5.3 of Bishop |
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Neural networks (cont'd) |
Slides | |||

Feature construction and selection |
Slides | |||

Deep learning |
Slides See last slide for readings, tutorials and code suggestions. | |||

Problem solving session |
See Discussion board on myCourses (Under topic "Midterme exam") for the practice questions. Solutions will be posted there in a few days. | |||

Unsupervised learning |
Slides Mini-project #3 due. |
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Online / streaming data |
Slides Final project available. |
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Semi-supervised learning |
Slides | |||

Parallelization for large-scale ML |
Slides | |||

Midterm (confirmed date). |
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Missing data. |
Slides | |||

Distributed Stochastic Convex OptimizationGuest speaker: Michael Rabbat |
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Final project presentation session |
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Wrap-up |
Final project report due on Dec.8. |