Lectures

• Lecture 1, Tuesday Aug 22nd: course introduction, What is clustering?.
• Lecture 2, Thursday Aug 24th: Clustering, Single-Link Algorithm. Lecture notes.
• Lecture 3, Tuesday Aug 29th: Single-Link Algorithm, K-means clustering. Lecture notes.
• Lecture 4, Thursday Aug 31st: K-means 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