Statistical Machine Learning CMU Spring 2016
Larry Wasserman

folder statistical_machine_learning_cmu_spring2016 (39 files)
fileReview.pdf 154.86kB
filesyllabus.pdf 118.69kB
filerandom_matrix_theory.pdf 190.86kB
filenonparclass.pdf 325.89kB
fileNonparRegression.pdf 650.95kB
fileminimax.pdf 353.06kB
filenonparbayes.pdf 313.51kB
filelinearclassification.pdf 2.43MB
fileLinearRegression.pdf 259.51kB
fileLecture 24 - Random Matrix Theory & Differential Privacy-ymqZMAvaVpw.mp4 687.92MB
fileLecture 23 - Dimension Reduction & Random Matrix Theory-3qRVzS-7MOs.mp4 852.05MB
fileLecture 22 - Dimension Reduction-xtgayLRW5RM.mp4 1.89GB
fileLecture 21 - Graphical Models-z4q8DMB6cXk.mp4 1.57GB
fileLecture 20 - Graphical Models-xNh1s6nql30.mp4 855.54MB
fileLecture 19 - Graphical Models-OcFMSWQstsE.mp4 1.58GB
fileLecture 17 - Clustering-D4kJkL3fyAg.mp4 1.62GB
fileLecture 18 - Graphical Models-9z0gebvwb3k.mp4 693.74MB
fileLecture 16 - Clustering-JhR6KdqQAxs.mp4 778.96MB
fileLecture 15 - Clustering-SRk90fwsZGQ.mp4 777.46MB
fileLecture 14 - Boosting-cWpYY2yqYmU.mp4 1.56GB
fileLecture 12 - Minimax Theory-UdoS-K2puu4.mp4 1.49GB
fileLecture 13 - Nonparametric Bayes--8kShV_24tM.mp4 1.22GB
fileLecture 11 - Minimax Theory-25mNchtk9eE.mp4 1.61GB
fileLecture 10 - Nonparametric Classification-3we__CnE-xQ.mp4 1.33GB
fileLecture 09 - Linear Classification-aB3P4yGvEOE.mp4 1.64GB
fileLecture 08 - Trend filtering-IXvTUiYB1Ec.mp4 1.63GB
fileLecture 07 - Nonparametric Regression-XB6uu-sHUMM.mp4 703.65MB
fileLecture 05 - Linear Regression-eltYWhGh9wY.mp4 1.51GB
fileLecture 06 - Nonparametric Regression-e9mN6UH5QIQ.mp4 1.57GB
fileLecture 04 - Concentration of Measure-wbyKfeQMrTc.mp4 1.47GB
fileLecture 03 - Concentration of Measure-WalRR9lcZcc.mp4 603.08MB
fileLecture 01 - Review-zcMnu-3wkWo.mp4 232.05MB
fileLecture 02 - Function Spaces-M6628Cm12YQ.mp4 297.19MB
filefunctionspaces.pdf 224.61kB
fileGraphicalModels.pdf 1.99MB
filedimension_reduction.pdf 10.27MB
fileConcentration-of-Measure.pdf 282.22kB
filedensityestimation.pdf 1.45MB
fileclustering.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= {}
}


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