Statistical Machine Learning CMU Spring 2016
Larry Wasserman

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Folderstatistical_machine_learning_cmu_spring2016
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clustering.pdf16.64MB
Concentration-of-Measure.pdf282.22kB
densityestimation.pdf1.45MB
dimension_reduction.pdf10.27MB
functionspaces.pdf224.61kB
GraphicalModels.pdf1.99MB
Lecture 01 - Review-zcMnu-3wkWo.mp4232.05MB
Lecture 02 - Function Spaces-M6628Cm12YQ.mp4297.19MB
Lecture 03 - Concentration of Measure-WalRR9lcZcc.mp4603.08MB
Lecture 04 - Concentration of Measure-wbyKfeQMrTc.mp41.47GB
Lecture 05 - Linear Regression-eltYWhGh9wY.mp41.51GB
Lecture 06 - Nonparametric Regression-e9mN6UH5QIQ.mp41.57GB
Lecture 07 - Nonparametric Regression-XB6uu-sHUMM.mp4703.65MB
Lecture 08 - Trend filtering-IXvTUiYB1Ec.mp41.63GB
Lecture 09 - Linear Classification-aB3P4yGvEOE.mp41.64GB
Lecture 10 - Nonparametric Classification-3we__CnE-xQ.mp41.33GB
Lecture 11 - Minimax Theory-25mNchtk9eE.mp41.61GB
Lecture 12 - Minimax Theory-UdoS-K2puu4.mp41.49GB
Lecture 13 - Nonparametric Bayes--8kShV_24tM.mp41.22GB
Lecture 14 - Boosting-cWpYY2yqYmU.mp41.56GB
Lecture 15 - Clustering-SRk90fwsZGQ.mp4777.46MB
Lecture 16 - Clustering-JhR6KdqQAxs.mp4778.96MB
Lecture 17 - Clustering-D4kJkL3fyAg.mp41.62GB
Lecture 18 - Graphical Models-9z0gebvwb3k.mp4693.74MB
Lecture 19 - Graphical Models-OcFMSWQstsE.mp41.58GB
Lecture 20 - Graphical Models-xNh1s6nql30.mp4855.54MB
Lecture 21 - Graphical Models-z4q8DMB6cXk.mp41.57GB
Lecture 22 - Dimension Reduction-xtgayLRW5RM.mp41.89GB
Lecture 23 - Dimension Reduction & Random Matrix Theory-3qRVzS-7MOs.mp4852.05MB
Lecture 24 - Random Matrix Theory & Differential Privacy-ymqZMAvaVpw.mp4687.92MB
linearclassification.pdf2.43MB
LinearRegression.pdf259.51kB
minimax.pdf353.06kB
nonparbayes.pdf313.51kB
nonparclass.pdf325.89kB
NonparRegression.pdf650.95kB
random_matrix_theory.pdf190.86kB
Review.pdf154.86kB
syllabus.pdf118.69kB
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