# 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
- 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.
- 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