Все публикации

0:02:01

TickScope screenshot real-time

0:34:00

Multivariate Analysis 18: Cluster Analysis Overview

0:37:38

Multivariate Analysis 15: MultiDimensional Scaling Code Example

0:54:18

Multivariate Analysis 14: MultiDimensional Scaling

0:53:09

Multivariate Analysis 13: Distances and Similarities

0:38:01

Multivariate Analysis 12: Interpreting Principal Components, and some general weaknesses of PCA

0:39:51

Multivariate Analysis 11: tuning loadings, and 3 examples: economics, genetics, and computer vision

0:53:10

Multivariate Analysis 10: Units and Standardization in principal component analysis

0:47:56

Multivariate Analysis 9: PCA, Singular Value Decomposition (SVD), and the pseudoinverse of a matrix

0:54:05

Multivariate Analysis 8: Principle Component Analysis Examples

1:03:42

Multivariate Analysis 7: Mathematics of Principal Component Analysis (PCA)

0:56:49

Multivariate Analysis 6: Motivating Principal Component Analysis (PCA)

1:03:13

Multivariate Analysis 5: Tools of Exploratory Data Analysis

0:31:24

Multivariate Analysis 4: Motivating Exploratory Data Analysis

0:50:37

Multivariate Analysis 3: Covariance matrix, correlation matrix, data cloud

0:49:36

Multivariate Analysis 2: vectors, eigenvectors, and eigenvalues

0:41:54

Multivariate Analysis 1: mathematical preliminaries, matrices and matrix algebra

1:12:14

Probability Lecture 23: random vectors, variance covariance matrix, and cross covariance

1:17:27

Probability Lecture 22: Covariance and Correlation

0:52:09

Probability Lecture 21: bivariate distributions for continuous random variables

0:42:28

Markov Chains Lecture 20: hidden Markov model forward algorithm

0:53:22

Probability Lecture 20: we see the bivariate mass function, and marginal distributions.

0:51:48

Markov Chains Lecture 19: hidden Markov model details

0:54:57

Probability Lecture 19: probability generating functions and moment generating functions