This material is from the Fall 2017 offering of the course. The syllabus, lecture notes, and homeworks that are provided to students are available via links. Additional resources available upon request.
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| Wk | Date | Topic | Stanford ML Notes [recommended] | Readings [recommended] | Assignments | Project |
|---|---|---|---|---|---|---|
| 1 | W 08/30 | - Introduction | - Lec 1 (Pgs 1-2) | - The Discipline of Machine Learning; Daume 1-1.2; LfD 1-1.2 | PS 1 out [Setup] [Written] [Programming] | |
| 2 | M 09/04 | - Decision Trees | - Daume 1.3-1.10; Flach 5-5.1 | |||
| W 09/06 | - k-Nearest Neighbors - Evaluation: Metrics |
- Daume 2-2.3, 2.5-2.6; Flach 8-8.3 - Daume 4.5; LfD 4.3; Flach 2.1,12 |
PS 1 due PS 2 out [Written] [Programming] |
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| 3 | M 09/11 | - Evaluation: Protocol - Linear Regression Setup |
- None - Lec 5 (Pgs 2-4) + Lec 1 (Pgs 3-13) |
- Daume 4.6-4.7 - Daume 6-6.2, 6.4-6.6; LfD 3.2-3.2.1; Flach 7.1 |
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| W 09/13 | - Linear Regression - Regularization |
- (see above) - None |
- (see above) - Daume 6.3; LfD 4-4.2 |
PS 2 due PS 3 out [Written] [Programming] |
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| 4 | M 09/18 | - Logistic Regression | - Lec 1 (Pgs 16-19) | - LfD 3.3 | ||
| W 09/20 | - Perceptron | - Lec 1 (Pg 19) + Lec 6 | - Daume 3; Flach 7.2 | PS 3 due PS 4 out [Written] [Programming] |
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| 5 | M 09/25 | - Support Vector Machines | - Lec 3 (Pgs 1-7, 19-20; optional 7-13) | - Daume 6.7; LfD 3.4; Flach 7.3 | ||
| W 09/27 | - Kernels | - Lec 3 (Pgs 13-29) | - Daume 9-9.2, 9.4-9.6; Flach | PS 4 due PS 5+6 out [Written] [Programming] |
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| 6 | M 10/02 | - Advanced Evaluation Metrics - Imbalanced Data |
- Daume 4.5 (review) - Daume 5-5.1; Flach 12.1 |
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| W 10/04 | - Multiclass Classification - Advice for Applying ML |
- None - "Advice for Applying ML" |
- Daume 5.2; Flach 3.1 - None |
PS 5 due PS 6 (Parts 1-3) "due" |
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| 7 | M 10/09 | - Ensemble Methods: Bagging | - Daume 11-11.1, 11.3; Flach 11-11.1 | Project out | ||
| W 10/11 | Midterm (in-class) | [brainstorm projects] | ||||
| 8 | M 10/16 | Fall Break | ||||
| W 10/18 | - Ensemble Methods: Boosting | - Daume 11.2; Flach 11.2 | PS 6 due PS 7 out [Written] [Programming] |
[brainstorm projects] | ||
| 9 | M 10/23 | - Dimensionality Reduction (PCA) | - Lec 10 | - Daume 13.2; Flach 10.3 | ||
| W 10/25 | - Clustering | - Lec 7a + Lec 7b | - Daume 2.4, 13-13.1; Flach 3.3, 8.4-8.5 | PS 7 due PS 8 out [Programming] |
Proposal Conference (by 5pm) | |
| 10 | M 10/30 | Project Proposal Presentations | ||||
| W 11/01 | Project Proposal Presentations | Proposal Writeup due Fri (5pm) | ||||
| 11 | M 11/06 | - Gaussian Mixture Models - Expectation Maximization |
- Sec Notes 7 (Pgs 1-2) - Lec 8 (Pgs 1-6) |
- Daume 14-14.1; Flach 9.4 - Daume 14.2 |
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| W 11/08 | - EM Applied to GMMs - Hidden Markov Models Overview |
- Lec 8 (Pgs 6-8) - Sec Notes 6 (Pgs 1-6) |
- None - Rabiner Tutorial (Pgs 257-262) |
PS 8 due PS 9 out [Written] |
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| 12 | M 11/13 | - HMMs: Inference | - Sec Notes 6 (Pgs 6-8) | - Rabiner Tutorial (Pgs 262-264) | ||
| W 11/15 | - HMMs: Learning | - Sec Notes 6 (Pgs 8-13) | - Rabiner Tutorial (Pgs 264-266) | Status Update due Fri (5pm) | ||
| 13 | M 11/20 | Project Conferences | ||||
| W 11/22 | - Computational Learning Theory: Finite Hypothesis Spaces | - Lec 4 (Pgs 1-8) | - Daume 10.1-10.5; LfD 1.3, 2.2-2.3 | PS 9 due PS 10 out [Written] |
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| 14 | M 11/27 | - Computational Learning Theory: Infinite Hypothesis Spaces | - Lec 4 (Pgs 8-11) | - Daume 10.6; LfD 2.1 | ||
| W 11/29 | - Special Topics - Course Evaluations |
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| 15 | M 12/04 | Project Presentations | ||||
| W 12/06 | Project Presentations | PS 10 due | Project Contributions due Fri (5pm) | |||