Lecture 23 - Dimension Reduction & Random Matrix Theory-3qRVzS-7MOs.mp4
852.05MB
Lecture 24 - Random Matrix Theory & Differential Privacy-ymqZMAvaVpw.mp4
687.92MB
linearclassification.pdf
2.43MB
LinearRegression.pdf
259.51kB
minimax.pdf
353.06kB
nonparbayes.pdf
313.51kB
nonparclass.pdf
325.89kB
NonparRegression.pdf
650.95kB
random_matrix_theory.pdf
190.86kB
Review.pdf
154.86kB
syllabus.pdf
118.69kB
Type: Course Tags: Bibtex:
@article{,
title= {Statistical Machine Learning CMU Spring 2016},
keywords= {},
journal= {},
author= {Larry Wasserman },
year= {},
url= {http://www.stat.cmu.edu/~larry/=sml/},
license= {},
abstract= {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 course covers methodology and theoretical foundations.
Function Spaces
Concentration of Measure
Linear Regression
Nonparametric Regression
Linear Classification
Nonparametric Classification
Minimax Theory
Density Estimation
Nonparametric Bayes
Clustering
Graphical Models
Dimension Reduction
Random Matrix Theory},
superseded= {},
terms= {}
}