Lectures

• Lecture 1, Tuesday Aug 23rd: course introduction, beginning of dimensionality reduction.
• Lecture 2, Thursday Aug 25rd: Dimensionality Reduction, Principal Component Analysis.
  Lecture notes for lectures 2 and 3.
  Scripts + data for demos in Matlab
• Lecture 3, Tuesday Aug 30th: Principal Component Analysis.
  Classroom example demo in Matlab
• Lecture 4, Thursday Sep 1st: Canonical Correlation Analysis.
  CCA Classroom example demo in Matlab
  Lecture notes for CCA
  • Warning regarding CCA implementation in scikit-learn (python)
  • Canonical correlation: A tutorial, by Magnus Borga, dated Jan 2001. Not too gentle, but useful.
  • Analysis of factors and canonical correlations, Mans Thulin, dated 2011. Not too gentle, but gives a different perspective and an example.
• Lecture 5, Tuesday Sep 6th: Finish CCA, Random Projections.
  Lecture notes
• Lecture 6, Thursday Sep 8th: Finish Random Projections, Kernel PCA (non-linear projections).
  Kernel PCA lecture notes
• Lecture 7, Tuesday Sep 13th: Finish Kernel PCA, Clustering.
  Kernel PCA demo
• Lecture 8, Thursday Sep 15th: Clustering, K-Means.
• Lecture 9, Tuesday Sep 20th: Wrap-up K-Means, Signle link, start spectral clustering.
• Lecture 10, Thursday Sep 22nd: Spectral clustering.
• Lecture 11, Thuesday Sep 27th: Spectral clustering.
  References:
  • A tutorial on Spectral CLustering
  • Spectral Clustering a quick overview
• Lecture 12, Thursday Sep 29th: Gaussian Mixture Model.
• Lecture 13, Tuesday Oct 4th: Mixture Model, Lecture notes for gaussian mixture model and EM [lecnotes]
• Lecture 14, Thursday Oct 6th: Review Lecture.
• Lecture 15, Thursday Oct 13th: Probabilistic Modelling, Mixture of Multinomials, Lecture Notes
• Lecture 16, Tuesday Oct 18th: Latent Dirichlet Allocation
• Lecture 17, Thursday Oct 20th: Graphical Models (Bayesian Networks)
• Lecture 18, Tuesday Oct 25th: Hidden Markov Model
• Lecture 19, Thursday Oct 27th: Hidden Markov Model
• Lecture 20, Tuesday Nov 1st: Learning in Hidden Markov Model, variable elimination for general Bayesian networks
• Lecture 21, Thursday Nov 3rd: Inference in Bayesian networks
• Lecture 22, Thursday Nov 10th: Lecture Notes from Guest Lecture by Kilian Weinberger on SNE and TSNE
• Lecture 23, Tuesday Nov 15th: Meassage Passing and Parameter Estimation
• Lecture 24, Thursday Nov 17th: Approximate inference
• Lecture 25, Tuesday Nov 22nd: Inference via Sampling, Particle Filters
• Lecture 26, Tuesday Nov 29th: Fairness Transparency and Other Issues in ML
• Lecture 27, Thursday Dec 1st: Last Lecture