Lectures
# | DATE | TOPIC | NOTES | ||
---|---|---|---|---|---|
1 | Aug 26 | Introduction | Overview of topics and applications | (none) | |
2 | Aug 31 | Supervised Learning | Linear Regression: gradient descent, Normal equations. | ||
3 | Sep 2 | Supervised Learning | Probabilistic Interpretation, Logistic Regression | ||
4 | Sep 7 | Supervised Learning | Newton's method, Locally weighted Linear Regression, Nearest Neighbors | (previous pdf) | |
5 | Sep 9 | Supervised Learning | Exponential Families, Generalized Linear Models | (previous pdf), Optional: Paper |
|
6 | Sep 14 | Optimization | Convex functions, Convex problems | pdf (pages 1-24), pdf (pages 1-23) |
|
7 | Sep 16 | Supervised Learning | Generative Learning Algorithms, Gaussian Discriminant Analysis | ||
8 | Sep 21 | Supervised Learning | Generative (contd.), Model and feature selection | Feature selection | |
9 | Sep 23 | Supervised Learning | Kernels | Parts of Bishop, ch 6. Parts of Notes |
|
10 | Sep 28 | Supervised Learning | SVM. | Duality (pages 1-6, 8-13) | |
11 | Sep 30 | Unsupervised Learning | Curse of Dimensionality, Dimensionality Reduction, PCA | Bishop, ch 12. (or PCA notes) | |
12 | Oct 14 | Adaboost | Object Detection | PPTX | |
13 | Oct 19 | Unsupervised Learning | Mixture of Gaussians, EM | ||
14 | Oct 21 | Unsupervised Learning | examples of EM, clustering, spectral clustering | PDF,
k-means notes. Spectral. |
|
-- | Oct 22, 2-4pm | Mid-term Project presentation | Details | ||
15 | Oct 26 | Unsupervised Learning | Multi-dimensional Scaling (MDS), Isomaps | Isomap paper | |
15 | Oct 28 | Review | Independent Component Analysis (ICA), Learning Review | ||
-- | Oct 28 | Mid-term Exam, 7:30-10pm | Supervised+Unsupervised Learning+Optimization+Theory | THR 203 | |
18 | Nov 2 | Unsupervised Learning | Non Negative Matrix Factorization | Bishop, ch 12. (Partial/complementary material covered here.) Full details in Prob PCA paper. NNMF Paper |
|
17 | Nov 4 | ||||
19 | Nov 9 | Probabilistic Graphical Models | Introduction, Representation, Markov Blanket, variable elimination |
Bishop, ch 8. Others (not necessarily relating directly to the lecture notes): html, pdf |
|
20 | Nov 11 | Probabilistic Graphical Models | HMM, Inference on a chain (sum-product specific case) | Bishop, ch 8. | |
21 | Nov 16 | Probabilistic Graphical Models | Kalman Filters | Bishop, ch 8. slides | |
22 | Nov 18 | Probabilistic Graphical Models | Directed / Undirected graphs, MRFs | Bishop, ch 8. | |
23 | Nov 23 | Probabilistic Graphical Models | Sum-product, Max-product | Bishop, ch 8. | |
24 | Nov 23 | Probabilistic Graphical Models | Examples. MRF: discrete (image-denoising), continuous (depth estimation), sampling/particle filters | Optional reading: Paper, Paper | |
25 | Nov 30 | Paper reading | Graphical Models | Paper 1, Paper 2 | |
25 | Dec 2 | Special Topic | Deep Learning | Paper 1, Paper 2 | |
- | Dec 1-10 | Peer Review Period | Review 2 other reports | ||
- | Dec 17, Friday | 2-5pm | Projects |