# Lectures

- Lecture 1: First Lecture: course introduction, logistics, beginning of dimensionality reduction.
- Lecture 2: Dimensionality Reduction, Principal Component Analysis.

Lecture notes for lectures 2 and 3.

Scripts + data for demo .

Handout for Lecture 2. - Lecture 3: Dimensionality Reduction, Principal Component Analysis.

Lecture notes for lectures 2 and 3.

Scripts + data for demo .

Handout for Lecture 3. - Lecture 4: Principal Component Analysis and Random Projections.

Lecture notes for Random Projections. Handout for Lecture 4.

Scripts + data for demo . - Lecture 5: Random Projections and Canonical Components Analysis.

Lecture notes for Random Projections. Handout for Lecture 4 and 5.

Scripts + data for demo . - Lecture 6: Canonical Components Analysis.

Lecture notes for CCA. Handout for Lecture 6. - Lecture 7: Canonical Components Analysis + Kernel PCA.

Lecture notes for Kernel PCA.

Demo . - Lecture 8: Kernel PCA + Isomap .

Handouts.

Demo . - Lecture 9: Isomap and t-SNE.

Handouts.

Demo . - Lecture 10: Spectral Embedding.

Handouts.

Demo . - Lecture 11: Spectral Embedding + Clustering.
- Lecture 12: Clustering + Linkage Clustering.

Handouts

Demo . - Lecture 13: K-Means Clustering.

Lecture Notes. - Lecture 14: Gaussian Mixture Model.

Lecture Notes.

Demo . - Lecture 15: Gaussian Mixture Model.

Handout.

Demo . - Lecture 16: EM Algorithm, Mixture models
- Lecture 17: EM Algorithm, Mixture models
- Lecture 18: EM Algorithm, Mixture models, Mixture of Multinomials
- Lecture 19: Hidden Markov Models
- Lecture 20: Hidden Markov Models: Baum Welch Algorithm

Demo .

Lecture notes on HMM . - Lecture 21: Approximate Inference Via Sampling and Particle Filtering
- Lecture 22: Particle Filtering + General Bayesian Netowrks
- Lecture 23: Differential Privacy
- Lecture 24: Differential Privacy and Reusable Holdout set
- Lecture 25: Fairness in Machine Learning
- Lecture 26: Fairness Continued and Polarization in Recommendations
- Lecture 27: Last Lecture