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.

VIDEOS:

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
9. PRACTICE PROBLEMS
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

h/t John Myles White and priya joseph

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