Lectures
 Lecture 1, Tuesday Aug 22nd: course introduction, What is clustering?.
 Lecture 2, Thursday Aug 24th: Clustering, SingleLink Algorithm.
Lecture notes.
 Lecture 3, Tuesday Aug 29th: SingleLink Algorithm, Kmeans clustering.
Lecture notes.
 Lecture 4, Thursday Aug 31st: Kmeans clustering
Matlab demo script here..
 Lecture 5, Tuesday Sep 5th: Gaussian Mixture Models
 Lecture 6, Tuesday Sep 7th: Gaussian Mixture Models
 Lecture 7, Tuesday Sep 12th: Gaussian Mixture Models
 Lecture 8, Thrusday Sep 14th: Mixture Models and Dimensionality Reduction
Demo code matlab here.
Lecture notes for Ellipsoidal clustering and Gaussian mixture models.
 Lecture 9, Tuesday Sep 19th: Principal Components Analysis (PCA)
Demo code matlab here.
Lecture notes for PCA.
 Lecture 10, Thursday Sep 21st: Principal Components Analysis (PCA) and Random Projections
Lecture notes for random projections.
Sample code for smiley faces here.
 Lecture 11, Tuesday Sep 26th: Random Projections + CCA
Lecture notes for random projections.
 Lecture 12, Thursday Sep 28th: CCA + kernel PCA
Lecture notes for Cannonical Correlation Analysis.
 Lecture 13, Tuesday Oct 3rd: kernel PCA + Spectral Clustering
Lecture notes for Kernel PCA
 Lecture 14, Thursday Oct 5th: Spectral Clustering
Lecture notes for Spectral Clustering

Lecture 15, Thursday Oct 12th: Review + Probabilistic Modeling

Lecture 16, Tuesday Oct 17th: Probabilistic Modeling + EM Algorithm
Lecture notes for Lectures 16 and 17

Lecture 17, Thursday Oct 19th: EM Algorithm, Mixture of Multinomials, Latent Dirchlet Allocations
Lecture notes for Lectures 16 and
17

Lecture 18, Tuesday Oct 24th: Graphical Models
Lecture notes for Lectures 18 and 19

Lecture 19, Thursday Oct 26th: Hidden Markov Models
Lecture notes for Lectures 19 and 20

Lecture 20, Tuesday Oct 31st: Hidden Markov Models
Lecture notes for Lectures 19 and 20

Lecture 21, Thursday Nov 2nd: Inference in Graphical Models
Lecture notes for Lectures 21 and 22

Lecture 22, Tuesday Nov 7th: Inference in Graphical Models
Lecture notes for Lectures 21 and 22

Lecture 23, Thursday Nov 9th: Approximate Inference in Graphical Models
Lecture notes for Lecture 23

Lecture 24, Tuesday Nov 14th: Guest Lecture by Prof. Kilian Weinberger

Lecture 25, Thursday Nov 16th: Approximate inference, Particle Filter for HMM

Lecture 26, Tuesday Nov 21st: Differential Privacy in ML

Lecture 27, Tuesday Nov 28th: Socially Responsible ML