Tuesday, August 11, 2015

Videos and Slides: Statistical Machine Learning course at CMU, Ryan Tibshirani and Larry Wasserman, Spring 2015

Here are the videos and handouts of this spring's Statistical Machine Learning at CMU taught by Ryan Tibshirani and  Larry Wasserman
Class Assistant: Mallory Deptola, Teaching Assistants: Sashank Reddi, Jisu Kim, Hanzhang Hu, Shashank Srivastava

Statistical Machine Learning is a second graduate level course in advanced machine learning , assuming students have taken Machine Learning (10-715) and Intermediate Statistics (36-705). The term "statistical" in the title reflects the emphasis on statistical theory and methodology.

The course combines methodology with theoretical foundations and computational aspects. It treats both the "art" of designing good learning algorithms and the "science" of analyzing an algorithm's statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own research.

  1. Video Lecture 1 Review Part 1
  2. Video Lecture 2 Review Part 2
  3. Video Lecture 3 Density Estimation
  4. Video Lecture 4 Density Estimation
  5. Video Lecture 5 Clustering
  6. Video Lecture 6 Clustering
  7. Video Lecture 7 Nonparametric Regression
  8. Video Lecture 8 Nonparametric Regression
  9. Video Lecture 9 Nonparametric Regression
  10. Video Lecture 10 Nonparametric Regression
  11. Video Lecture 11 Nonparametric Bayes
  12. Video Lecture 12 Sparsity
  13. Video Lecture 13 Sparsity
  14. Video Lecture 14 Graphical Models
  15. Video Lecture: review class Midterm Review
  16. Video Lecture 15 Graphical Models
  17. Video Lecture 16 Convexity
  18. Video Lecture 17 Convexity
  19. Video Lecture 18 Concentration of Measure
  20. Video Lecture 19 Concentration of Measure
  21. Video Lecture 20 Minimax
  22. Video Lecture 21 Minimax
  23. Video Lecture 22 Stein
  24. Video Lecture 23 Stein
  25. Video Lecture 24 Active Learning
  26. Video Lecture 25 The Truth

HANDOUTS: Syllabus

  1. Review Part 1
  2. Review Part 2
  3. Density Estimation
  4. Clustering
  5. Nonparametric Regression
  6. Bayes/Frequentist
  7. Nonparametric Bayes
  8. Sparsity
  10. Graphical Models
  11. Convex Optimization
  12. Concentration of Measure
  13. Minimax Theory
  14. Stein's Unbiased Risk Estimate
  15. Active Learning
  16. The Truth
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