"Unsupervised Machine Learning"
Fall 2016
| No. | Date | Topics | Notes | Lab | Homework |
|---|---|---|---|---|---|
| 1 | Thurs, Sept 8 | Introduction to machine learning | Slides 1 Reading: ESL, Chap. 1 |
Lab 0: Setting up Python | |
| 2 | Tues, Sept 13 | Nearest neighbors and regression | Board Photos 2 Reading: ESL, Sections 2.1-2.3 |
Homework 1: Supervised Learning |
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| 3 | Thurs, Sept 15 | (cont) | Board Photos 3 Numpy Notes |
Lab 1: Nearest neighbors | |
| 4 | Tues, Sept 20 | Introduction to clustering | Board Photos 4 Reading: ESL, Section 14.3 (through 14.3.4) |
Homework 2: Clustering |
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| 5 | Thurs, Sept 22 | Hierarchical clustering: UPGMA | Board Photos 5 Handout 1 Reading: ESL, Section 14.3.12 |
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| 6 | Tues, Sept 27 | K-means clustering | Board Photos 6 Reading: ESL, Section 14.3.6 |
Lab 2: K-means |
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| 7 | Thurs, Sept 29 | (cont) | Board Photos 7 Handout 2 Reading: ESL, Section 14.3.11 |
Homework 3: Clustering Literature | |
| 8 | Tues, Oct 4 | Principal Components Analysis (PCA) | Board Photos 8 Reading: ESL, Section 14.5.1 Tutorial: PCA tutorial |
Lab 3: PCA |
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| 9 | Thurs, Oct 6 | Autoencoders | Slides 9 Board Photos 9 |
Homework 4: Dimensionality Reduction |
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| - | Tues, Oct 11 | AUTUMN RECESS | |||
| 10 | Thurs, Oct 13 | Clustering paper discussion | Slides 10 | ||
| 11 | Tues, Oct 18 | Finish paper discussion Start probability and statistics |
Slides 11 Board Photos 11 |
Midterm Midterm Solution |
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| 12 | Thurs, Oct 20 | Special Topic: Deep Learning for Population Genetics |
Slides 12 | ||
| 13 | Tues, Oct 25 | Introduction to graphical models | Board Photos 13 | Homework 5: Paper Presentation | |
| 14 | Thurs, Oct 27 | Hidden Markov Models (HMMs) | Slides 14 Board Photos 14 Reading: HMM Introduction |
Lab 4: Markov Chains |
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| 15 | Tues, Nov 1 | Midterm presentations: Jessica Tin, Jackie, Li, Youyou |
Midterm Presentation Slides | ||
| 16 | Thurs, Nov 3 | Midterm presentations: Lujun, Farida, Sharon, Hera, Jessica Tran |
Midterm Presentation Slides | ||
| 17 | Tues, Nov 8 | Midterm presentations: Jenny, Alice, Isha, Yvaine, Karen |
Midterm Presentation Slides | Homework 6: Project Proposal | |
| 18 | Thurs, Nov 10 | Midterm presentations: Deepshikha, Sarah, Maria, Amelia, Ravinder |
Midterm Presentation Slides | ||
| 19 | Tues, Nov 15 | Midterm presentations: Zoe Expectation-maximization (EM) for HMM |
Midterm Presentation Slides Slides 19 Board Photos 19 |
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| 20 | Thurs, Nov 17 | EM for Mixture of Gaussians | Slides 20 Board Photos 20 Reading: EM for GMM |
Homework 7: Hidden Markov Models |
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| 21 | Tues, Nov 22 | Neural networks | Board Photos 21 Reading: Stanford Course Notes (Module 1) Tutorial: UFLDL Tutorial |
Lab 5: Gaussian Mixture Models |
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| - | Thurs, Nov 24 | THANKSGIVING RECESS | |||
| 22 | Tues, Nov 29 | Deep learning | Board Photos 22 Reading: Stanford Course Notes (Module 2) |
Final Presentations | |
| 23 | Thurs, Dec 1 | Ethics in AI discussion | Board Photos 23 Reading: Feldman et al. Video: KDD 2015 talk |
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| 24 | Tues, Dec 6 | Final presentations: Deepshikha, Amelia, Yvaine, Isha, Alice |
Final Presentation Slides | ||
| 25 | Thurs, Dec 8 | Final presentations: Zoe, Ravinder, Jessica Tin, Maria, Sarah |
Final Presentation Slides | ||
| 26 | Tues, Dec 13 | Final presentations: Lujun, Youyou, Li, Jackie, Karen |
Final Presentation Slides | ||
| 27 | Thurs, Dec 15 | Final presentations: Jenny, Jessica Tran, Hera, Sharon, Farida |
Final Presentation Slides | Final Project Writeup | |