Statistical Machine Learning

Overview Statistical Machine Learning,10-702/36-702, is a second graduate level course in advanced machine learning. The term statistical in the title reflects the emphasis on statistical theory and methodology. The course combines methodology with theoretical foundations. 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. The course includes topics in statistical theory that are important for researchers in machine learning, including nonparametric theory, consistency, minimax estimation, and concentration of measure.

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Created by Admin corner
Last updated Mon, 27-Jun-2022
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Course overview
Lecture Details

Lecture Date: Jan 17, 2017.
http://www.stat.cmu.edu/~ryantibs/statml/

Curriculum for this course
22 Lessons 26:51:56 Hours
Lecture
22 Lessons 26:51:56 Hours
  • Linear regression
    Preview 01:10:06
  • Linear classification
    01:16:04
  • Nonparametric regression
    01:17:10
  • Nonparametric regression
    01:03:30
  • Nonparametric classification1
    01:15:45
  • Nonparametric classification2
    01:16:58
  • RKHS
    00:52:08
  • Density estimation
    01:17:20
  • Clustering
    01:21:12
  • Clustering1
    01:20:18
  • Clustering2
    01:17:29
  • High Dimensional Testing
    01:18:30
  • Concentration of Measure
    01:09:54
  • Concentration of measure1
    01:16:56
  • Minimax theory
    01:19:07
  • Minimax theory1
    01:16:01
  • Sparsity and the lasso
    01:07:46
  • Sparsity and the lasso1
    01:03:36
  • Sparsity and the lasso2
    01:14:23
  • Sparsity and the lasso3
    01:13:39
  • Graphical models
    01:09:16
  • Graphical models1
    01:14:48
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