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