45:50

Analysis of Categorical Variables Part 2

26:53

Comparing Two Group Means

01:10:16

Advanced Topic Models Part 2

38:41

Introduction to Advanced Topic Modeling in Machine Learning

54:38

Association between Two Categorical Variables Part 1

26:23

MATH 280: Integral Calculus

44:35

Gaussian Graphical -LASSO in R

44:48

Graphical- LASSO for learning Sparse Covariance Matrix

41:40

STAT 4051:Graphical- LASSO

34:37

Follow up to ANOVA F Test

45:31

ANOVA F Test Statistic

45:57

Comparing More Than Two Groups

33:43

Applications of Derivative

22:03

MATHS 280: Chain Rule in Diffentiation

20:35

MATH 280:Derivation of Trigonometric functions

48:27

Inference in Bayesian Networks Part 2

51:06

Equivalence of Hypothesis Testing and Confidence Interval

14:14

MATH 280:Derivative of Exponential Function

23:48

MATH 280:Derivative of Logarithmic Function

51:46

Factor Analysis

47:20

Confidence Interval for Population Mean

28:08

STAT 4051:MDS & ISO Map Part 2

26:51

STAT 4051: Multidimensional Scaling (MDS) Part 1

51:55

Confidence Interval of Proportion Part 2

49:12

Confidence Interval of Proportions Part 1

44:50

SPARSE Principal Component Analysis

54:47

STAT 4051: Matrix Completion in Machine Learning

53:32

Matrix Factorization in Machine Learning

51:06

STAT 3011 Midterm 1 Preview Part 1

58:25

Sampling distribution of Proportions

46:29

Sampling distribution of Means Part 2

44:59

STAT 4051 Exam 1 Practice Part 2

46:19

Sampling Distribution of Means Part 1

56:45

STAT 4051 Exam 1 Practice Part 1

47:52

Application of PCA Part 2

48:15

The Binomial Distribution

49:46

Sampling Distribution of Statistic

16:02

Real life Applications of Principal Components Analysis

49:17

Gaussian Mixture Model based Clustering

49:19

Normal Distribution Application in Real Life

52:00

Density Based Spatial Clustering of Application with Noise

48:59

Probability Distribution in Real life Applications

50:37

K-Medoid and Hierarchical Clustering Algorithms

01:05:54

Probability Concept

50:13

K-Means Algorithm for Clustering

48:00

Probability in everyday life

58:08

Clustering in Machine Learning

51:41

Data Gathering: Observation vs Experiment

53:55

STAT 4051:Theory of Convex Functions

56:37

Further Topics in Data Exploration

52:49

Numerical Summaries of Quantitative Data

48:52

STAT 4051: Maths Review

07:15

STAT 4051 : Statistical Machine Learning Course Introduction

53:25

Data Exploration: Graphical Summaries of Data

48:04

Introduction to Statistical Analysis

30:14

Day 3: Academic Dishonesty

24:08

Model Selection in Time Series

14:58

Final Exam Practice Discrete Probability Function

15:41

Final Exam Practice Multivariate Normal

05:21

Final Exam Practice MGF Part 2