Все публикации

0:11:47

AY2122-STO13 - Lecture 2 - Types of Variables

0:46:40

AY21-22-STO13 - Lecture 6 - Basics of Probability Theory

0:31:46

AY21-22- STO13-Lecture 19 - knn using R - Part 1

0:28:24

AY21-22-STO13-Lecture 23-Basic Principles of CART

0:16:39

AY21-22-STO13 -Lecture 34 - Random Forest

0:25:05

AY21-22- STO13-Lecture 16 - Parametric and Nonparametric Bootstrap in R

0:21:45

AY21-22-STO13-Lecture 22- Introduction to Classification and Regression Trees

0:25:16

AY21-22-STO13 - Lecture 7 - Basics of Inference

0:22:00

AY21-22-STO13 - Lecture 8 - Nonparametric Bootstrap

0:05:32

AY21-22-STO13 - Lecture 3 - Predictors and Response

0:12:42

AY21-22-STO13-Lecture 27 - Categorical Predictors in CART

0:42:59

AY21-22-STO13-Lecture 30 - Feature Selection 1

0:25:42

AY21-22-STO13-Lecture 39 - Maximal Margin Classifier

0:16:00

AY21-22-STO13-Lecture 28 - Miscellaneous issues in CART

0:18:09

AY21-22-STO13-Lecture 4 - Why Data Mining?

0:17:20

AY21-22-STO13-Lecture 33 - Bagging

0:21:45

AY21-22-STO13-Lecture 40 - Support Vector Classifier

0:42:16

AY21-22-STO13-Lecture 17 - Crossvalidation using R

0:50:43

AY21-22-STO13-Lecture 35 - Tackling Multiplicity Issues

0:22:38

AY21-22-STO13-Lecture 36 - Boosting in Regression Trees

0:42:17

AY21-22-STO13-Lecture 18 - Effect of Model Flexibility on Errors using R

0:28:35

AY21-22-STO13-Lecture 32 - Multiplicity Issue in Testing of Hypotheses

0:30:12

AY21-22-STO13- Lecture 29 - Feature extraction

0:14:15

AY21-22-STO13-Lecture 11 - Jackknife