statistical_machine_learning_cmu_spring2016 (39 files)
 Review.pdf |
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| syllabus.pdf |
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| random_matrix_theory.pdf |
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| nonparclass.pdf |
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| NonparRegression.pdf |
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| minimax.pdf |
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| nonparbayes.pdf |
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| linearclassification.pdf |
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| LinearRegression.pdf |
259.51kB |
| Lecture 24 - Random Matrix Theory & Differential Privacy-ymqZMAvaVpw.mp4 |
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| Lecture 23 - Dimension Reduction & Random Matrix Theory-3qRVzS-7MOs.mp4 |
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| Lecture 22 - Dimension Reduction-xtgayLRW5RM.mp4 |
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| Lecture 21 - Graphical Models-z4q8DMB6cXk.mp4 |
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| Lecture 20 - Graphical Models-xNh1s6nql30.mp4 |
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| Lecture 19 - Graphical Models-OcFMSWQstsE.mp4 |
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| Lecture 17 - Clustering-D4kJkL3fyAg.mp4 |
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| 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 |
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| Lecture 12 - Minimax Theory-UdoS-K2puu4.mp4 |
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| Lecture 13 - Nonparametric Bayes--8kShV_24tM.mp4 |
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| Lecture 11 - Minimax Theory-25mNchtk9eE.mp4 |
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| Lecture 10 - Nonparametric Classification-3we__CnE-xQ.mp4 |
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| Lecture 09 - Linear Classification-aB3P4yGvEOE.mp4 |
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| Lecture 08 - Trend filtering-IXvTUiYB1Ec.mp4 |
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| Lecture 07 - Nonparametric Regression-XB6uu-sHUMM.mp4 |
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| Lecture 05 - Linear Regression-eltYWhGh9wY.mp4 |
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| Lecture 06 - Nonparametric Regression-e9mN6UH5QIQ.mp4 |
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| Lecture 04 - Concentration of Measure-wbyKfeQMrTc.mp4 |
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| Lecture 03 - Concentration of Measure-WalRR9lcZcc.mp4 |
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| 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
Bibtex:
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= {}
}
