Name: Statistical Machine Learning CMU Spring 2016
Creator: Larry Wasserman
License: https://academictorrents.com/nolicensespecified

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07f1555918ed051809f0075fedc0cd469a194c93

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2:51 ago

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statistical_machine_learning_cmu_spring2016

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39 files

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Path	Size
clustering.pdf	16.64MB
Concentration-of-Measure.pdf	282.22kB
densityestimation.pdf	1.45MB
dimension_reduction.pdf	10.27MB
functionspaces.pdf	224.61kB
GraphicalModels.pdf	1.99MB
Lecture 01 - Review-zcMnu-3wkWo.mp4	232.05MB
Lecture 02 - Function Spaces-M6628Cm12YQ.mp4	297.19MB
Lecture 03 - Concentration of Measure-WalRR9lcZcc.mp4	603.08MB
Lecture 04 - Concentration of Measure-wbyKfeQMrTc.mp4	1.47GB
Lecture 05 - Linear Regression-eltYWhGh9wY.mp4	1.51GB
Lecture 06 - Nonparametric Regression-e9mN6UH5QIQ.mp4	1.57GB
Lecture 07 - Nonparametric Regression-XB6uu-sHUMM.mp4	703.65MB
Lecture 08 - Trend filtering-IXvTUiYB1Ec.mp4	1.63GB
Lecture 09 - Linear Classification-aB3P4yGvEOE.mp4	1.64GB
Lecture 10 - Nonparametric Classification-3we__CnE-xQ.mp4	1.33GB
Lecture 11 - Minimax Theory-25mNchtk9eE.mp4	1.61GB
Lecture 12 - Minimax Theory-UdoS-K2puu4.mp4	1.49GB
Lecture 13 - Nonparametric Bayes--8kShV_24tM.mp4	1.22GB
Lecture 14 - Boosting-cWpYY2yqYmU.mp4	1.56GB
Lecture 15 - Clustering-SRk90fwsZGQ.mp4	777.46MB
Lecture 16 - Clustering-JhR6KdqQAxs.mp4	778.96MB
Lecture 17 - Clustering-D4kJkL3fyAg.mp4	1.62GB
Lecture 18 - Graphical Models-9z0gebvwb3k.mp4	693.74MB
Lecture 19 - Graphical Models-OcFMSWQstsE.mp4	1.58GB
Lecture 20 - Graphical Models-xNh1s6nql30.mp4	855.54MB
Lecture 21 - Graphical Models-z4q8DMB6cXk.mp4	1.57GB
Lecture 22 - Dimension Reduction-xtgayLRW5RM.mp4	1.89GB
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

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statistical_machine_learning_cmu_spring2016 (39 files)

Review.pdf	154.86kB
syllabus.pdf	118.69kB
random_matrix_theory.pdf	190.86kB
nonparclass.pdf	325.89kB
NonparRegression.pdf	650.95kB
minimax.pdf	353.06kB
nonparbayes.pdf	313.51kB
linearclassification.pdf	2.43MB
LinearRegression.pdf	259.51kB
Lecture 24 - Random Matrix Theory & Differential Privacy-ymqZMAvaVpw.mp4	687.92MB
Lecture 23 - Dimension Reduction & Random Matrix Theory-3qRVzS-7MOs.mp4	852.05MB
Lecture 22 - Dimension Reduction-xtgayLRW5RM.mp4	1.89GB
Lecture 21 - Graphical Models-z4q8DMB6cXk.mp4	1.57GB
Lecture 20 - Graphical Models-xNh1s6nql30.mp4	855.54MB
Lecture 19 - Graphical Models-OcFMSWQstsE.mp4	1.58GB
Lecture 17 - Clustering-D4kJkL3fyAg.mp4	1.62GB
Lecture 18 - Graphical Models-9z0gebvwb3k.mp4	693.74MB
Lecture 16 - Clustering-JhR6KdqQAxs.mp4	778.96MB
Lecture 15 - Clustering-SRk90fwsZGQ.mp4	777.46MB
Lecture 14 - Boosting-cWpYY2yqYmU.mp4	1.56GB
Lecture 12 - Minimax Theory-UdoS-K2puu4.mp4	1.49GB
Lecture 13 - Nonparametric Bayes--8kShV_24tM.mp4	1.22GB
Lecture 11 - Minimax Theory-25mNchtk9eE.mp4	1.61GB
Lecture 10 - Nonparametric Classification-3we__CnE-xQ.mp4	1.33GB
Lecture 09 - Linear Classification-aB3P4yGvEOE.mp4	1.64GB
Lecture 08 - Trend filtering-IXvTUiYB1Ec.mp4	1.63GB
Lecture 07 - Nonparametric Regression-XB6uu-sHUMM.mp4	703.65MB
Lecture 05 - Linear Regression-eltYWhGh9wY.mp4	1.51GB
Lecture 06 - Nonparametric Regression-e9mN6UH5QIQ.mp4	1.57GB
Lecture 04 - Concentration of Measure-wbyKfeQMrTc.mp4	1.47GB
Lecture 03 - Concentration of Measure-WalRR9lcZcc.mp4	603.08MB
Lecture 01 - Review-zcMnu-3wkWo.mp4	232.05MB
Lecture 02 - Function Spaces-M6628Cm12YQ.mp4	297.19MB
functionspaces.pdf	224.61kB
GraphicalModels.pdf	1.99MB
dimension_reduction.pdf	10.27MB
Concentration-of-Measure.pdf	282.22kB
densityestimation.pdf	1.45MB
clustering.pdf	16.64MB

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= {}
}

Statistical Machine Learning CMU Spring 2016 Larry Wasserman

Statistical Machine Learning CMU Spring 2016
Larry Wasserman