| Topics | Lecture Notes | Reading | Problem Set | Solutions |
| Mon. 3/31 | Unsupervised vs. Supervised Learning; Clustering with k-means and k-medoids | Lecture 1 Slides | ESL 14.1, 14.3 (except 14.3.7, 14.3.12)
Optional: k-means++, Gap statistic, kd-trees | - | - |
| Weds. 4/2 | Gaussian Mixture Models; Expectation-Maximization | Lecture 2 Scribed Notes
Lecture 2 Slides | ESL 6.8, 8.5, 14.3.7
Mixture modeling chapter, EM chapter | - | - |
| Mon. 4/7 | Expectation-Maximization; General Mixture Modeling | Lecture 3 Scribed Notes | Mixture modeling chapter, EM chapter
Optional: Original EM paper | - | - |
| Weds. 4/9 | Discrete Hidden Markov Models | Lecture 4 Slides
Lecture 4 Scribed Notes | HMM chapter | Homework 1, Data | Homework 1 Solutions |
| Mon. 4/14 | Discrete HMMs; Hierarchical Clustering | Lecture 5 Scribed Notes | HMM chapter, ESL 14.3.12 | - | - |
| Weds. 4/16 | Hierarchical Clustering; Spectral Clustering | Lecture 6 Scribed Notes
Lecture 6 Slides | ESL 14.3.12, 14.5.3, Spectral clustering tutorial
Optional: Minimax linkage | - | - |
| Mon. 4/21 | Spectral Clustering; Linear Dimensionality Reduction via Principal Component Analysis | Lecture 7 Slides
Lecture 7 Scribed Notes | Spectral clustering tutorial, ESL 14.5.3, 14.5.1, 3.5.1
Optional: Normalized cuts | - | - |
| Weds. 4/23 | Principal Component Analysis; Kernel PCA | Lecture 8 Slides
Lecture 8 Scribed Notes | ESL 14.5.1, 3.5.1, 14.5.4
Kernel PCA | Homework 2, Data | Homework 2 Solutions |
| Mon. 4/28 | Kernel PCA; Factor Analysis | Lecture 9 Slides
Lecture 9 Scribed Notes | ESL 14.7.1, Multivariate Gaussian chapter, Factor analysis chapter | - | - |
| Weds. 4/30 | Factor Analysis; Linear Gaussian State-Space Models and Kalman Filtering | Lecture 10 Scribed Notes | Factor analysis chapter, State-space models chapter
Optional: Probabilistic PCA | - | - |
| Mon. 5/5 | Linear Gaussian SSMs | Lecture 11 Scribed Notes | State-space models chapter | - | - |
| Weds. 5/7 | SSMs; Independent Component Analysis; Canonical Correlation Analysis | Lecture 12 Slides
Lecture 12 Scribed Notes | ESL 14.7, 3.7, ICA | Homework 3, Data | Homework 3 Solutions |
| Mon. 5/12 | CCA; Sparse Unsupervised Learning | Lecture 13 Scribed Notes | ESL 3.7, 14.5.5, Exact and greedy sparse PCA | - | - |
| Weds. 5/14 | Sparse Unsupervised Learning | Lecture 14 Scribed Notes
Lecture 14 Slides | ESL 14.5.5, DSPCA
Optional: Deflation methods, Sparse clustering | Practice Midterm Questions | Practice Midterm Solutions |
| Mon. 5/19 | Unsupervised Deep Learning | Lecture 15 Slides
Lecture 15 Scribed Notes | Representation learning
Optional: Deep learning | - | - |
| Weds. 5/21 | In-class Midterm | - | - | Midterm | Midterm Solutions |
| Mon. 5/26 | Memorial Day - No Class | - | - | - | - |
| Weds. 5/28 | Learning with Missing Data | Lecture 16 Scribed Notes | ESL 9.6 | - | - |
| Mon. 6/2 | Unsupervised Learning with Missing Data | Lecture 17 Scribed Notes | Matrix factorization, Nuclear norm heuristic
Optional: Alternating minimization theory, Weighted trace norm | - | - |
| Weds. 6/4 | Final Project Presentations - Sequoia Hall Courtyard | - | - | - | -
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