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Lectures

# DATE TOPIC NOTES
1 Aug 27 Introduction Overview of topics and applications (none)
2 Sep 1 Supervised Learning Linear Regression: gradient descent, Normal equations, probabilistic interpretation PDF
3 Sep 3 Supervised Learning Logistic Regression, Generalized Linear Models Paper
4 Sep 8 Supervised Learning Generative Learning Algorithms, Gaussian Discriminant Analysis PDF
Optimization Convex functions, Convex problems pdf (pages 1-13),
pdf (pages 1-13)
5 Sep 10 Optimization Convex Functions, Convex Problems: LP, QP. pdf (pages 14-24),
pdf (pages 14-23)
6 Sep 15 Supervised Learning Kernels and SVMs Parts of Bishop, ch 6.
Parts of Notes
7 Sep 17 Supervised Learning SVMs. Duality (pages 1-6, 8-13)
Supervised Learning Testing Metrics and Model Selection (no notes)
8 Sep 22 Unsupervised Learning Curse of Dimensionality, Dimensionality Reduction, PCA Bishop, ch 12. (or PCA notes)
9 Sep 24 Unsupervised Learning prob. PCA, Non Negative Matrix Factorization Bishop, ch 12. (Partial/complementary material covered here.)
Full details in Prob PCA paper.
NNMF Paper
10 Sep 29 Unsupervised Learning Feature Selection, Multi-dimensional Scaling (MDS), Isomaps Feature selection
Isomap paper
11 Oct 1 Unsupervised Learning Mixture of Gaussians, EM PDF
12 Oct 6 Unsupervised Learning EM, examples of EM PDF
13 Oct 8 Unsupervised Learning Clustering, Spectral Clustering k-means notes.
Spectral.
14 Oct 15 Unsupervised Learning Independent Component Analysis (ICA), Learning Review PDF
-- Oct 20 Mid-term Exam Supervised+Unsupervised Learning+Optimization In class
15 Oct 22 Probabilistic Graphical Models Introduction, Representation, Markov Blanket Bishop, ch 8.
Others (not necessarily relating directly to the lecture notes): html, pdf
16 Oct 27 Probabilistic Graphical Models Variable elimination --
17 Oct 29 Probabilistic Graphical Models HMM, Inference on a chain (sum-product specific case) Bishop, ch 8.
18 Nov 3 Probabilistic Graphical Models Directed / Undirected graphs, conversion Bishop, ch 8.
19 Nov 5 Probabilistic Graphical Models Factor graphs, Sum-product Bishop, ch 8.
20 Nov 10 Probabilistic Graphical Models Sum-product, Max-product Bishop, ch 8.
21 Nov 12 Probabilistic Graphical Models Junction Trees, MRF: discrete (image-denoising), continuous (depth estimation) Optional reading: Paper, Paper
22 Nov 17 Probabilistic Graphical Models Markov Chains, stationary distribution --
23 Nov 19 Probabilistic Graphical Models Kalman Filters slides
24 Nov 24 Probabilistic Graphical Models Sampling, Particle Filters pdf
25 Dec 1 Probabilistic Graphical Models Gibbs sampling pdf
26 Dec 3 Probabilistic Graphical Models Priors
- Dec 15 Final project presentation Projects