# Lectures

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

Lecture notes for lectures 2 and 3.

Classroom example python demo .

Handout for Lecture 2. - Lecture 3: Principal Components Analysis.

Lecture notes for lectures 2 and 3.

Demo .

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

Lecture notes for Random Projections.

Demo .

Handout for Lecture 4. - Lecture 5: Random Projections and Canonical Components Analysis.

Lecture notes for CCA.

Handout for Lecture 5. - Lecture 6: Canonical Components Analysis.

Lecture notes for CCA.

Handout for Lecture 6.

Demo . - Lecture 7: Kernel PCA.

Lecture notes for Kernel PCA.

Handout for Lecture 7.

Demo . - Lecture 8: Kernel PCA and ISOMAP.

Lecture notes for Kernel PCA.

Handout for Lecture 8.

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

Lecture notes for ISOMAP and t-SNE.

Handout for Lecture 9.

Demo . - Lecture 10: t-SNE and Spectral Embedding.

Lecture notes for ISOMAP and t-SNE.

Handout for Lecture 10. - Lecture 11: Clustering: Single Linkage Clustering.

Lectures slides from Prof. Joachim's lecture.

Lecture notes for Clustering: Single Linkage and K-means.

Handout for Lecture 11.

Demo . - Lecture 12: K-Means Clustering.

Lectures slides from Prof. Joachim's lecture.

Lecture notes for Clustering: Single Linkage and K-means.

Demo (same as lecture 11). - Lecture 13: Spectral Embedding Continued.

Demo . - Lecture 14: Gaussian Mixture Model.

Lecture notes on Gaussian Mixture Models.

Gaussian Mixture Model Demo. - Lecture 15: Gaussian Mixture Model.

Lecture notes on Gaussian Mixture Models.

Gaussian Mixture Model Demo. - Lecture 16: Gaussian Mixture Model and EM algorithm.

Lecture notes on Gaussian Mixture Models.

Gaussian Mixture Model Demo.

Zoom Video recording: link for all the lectures only via canvas now! See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 17: Mixture Model and EM algorithm.

Zoom Video recording: link for all the lectures only via canvas now! See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 18: EM algorithm and Mixture of Multinomials.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 19: Hidden Markov Model.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 20: Hidden Markov Model.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 21: Hidden Markov Model.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 22: Hidden Markov Model.

Lecture notes on Hidden MArkov Models.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 23: Approximate Inference, Particle Filtering.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 24: Differential Privacy.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255 - Lecture 25: Reusable Holdout Set.

Zoom Video recording: link for all the lectures only via canvas now. See media tab under cs4786 course.

Zoom Meeting ID (only via Cornell id): 893-401-4255