24:35

Who Uses Machine Learning? 2024 Update

29:29

BADM Project Presentations: InterAgent

01:08:03

Prediction in Causal Research: Part 1/2

28:54

Prediction in Causal Research: Part 2/2

45:10

2021 Gosset Lecture: The Language of Statistics (and What's Lost in Translation)

58:26

"Improving" Prediction using Behavior Modification (Keynote at ISF 2021)

24:36

Yoga Session for 2020 International Conference on Information Systems (ICIS)

12:50

Introducing the IJDS for 2020 INFORMS DM+QSR Editors panel

10:24

Introducing the IJDS at the 2020 INFORMS joint Joint DMDA/DS Editorial Panel Session

03:14

2020 Teaching Innovation Award by INFORMS Information Systems Society: Award Speech by Galit Shmueli

37:30

"Improving" Prediction of Human Behavior using Behavior Modification (ENBIS-20)

59:03

ACEMS Public Lecture: "Improving" Prediction of Human Behavior Using Behavior Modification

15:08

Wrestling Prediction Error: Better Predictions or Better "Actuals"?

01:20:57

Reinventing the Data Analytics Classroom

13:36

BADM 5.3 Hierarchical Clustering Part 2

18:13

Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

16:13

Discriminant Analysis: Statistical Distance (Part 2)

21:00

Discriminant Analysis (Part 1)

10:44

Neural Networks: Part II

25:00

Neural Networks: Part I

16:58

BADM 12.2 Association Rules Part 2

15:28

BADM 12.1 Association Rules Part 1

10:34

BADM 11 Ensembles

09:45

BADM 10 Multi-Class Classification

18:33

BADM 9.2 Logistic Regression for Classification

13:20

BADM 9.1 Logistic Regression for Profiling

08:52

BADM 8.3 Classification and Regression Trees Part 3

16:34

BADM 8.2 Classification and Regression Trees Part 2

15:10

BADM 8.1 Classification and Regression Trees Part 1

19:21

BADM 7.2 Naive Bayes

19:11

BADM 7.1 K-Nearest Neighbors

11:25

BADM 6.1 Classification Goals

05:31

BADM 6.2 Classification Performance Part 1: The Naive Rule

13:03

BADM 6.3 Classification Performance Part 2

13:56

BADM 6.4 Classification Performance Part 3

17:04

BADM 5.4 K-Means Clustering

17:24

BADM 5.2 Hierarchical Clustering Part 1

12:58

BADM 5.1 Clustering Examples

13:37

BADM 4.4 Linear Regression for Prediction Part 2

13:34

BADM 4.3 Linear Regression for Prediction Part 1

13:02

BADM 4.2 Linear Regression for Descriptive Modeling Part 2

20:04

BADM 4.1: Linear Regression for Descriptive Modeling Part 1

15:01

BADM 3.1: PCA Part 1

06:46

BADM 3.3: Dimension Reduction Approaches

07:45

BADM 3.2: PCA Part 2

11:59

BADM 1.1: Data Mining Applications

11:04

BADM 1.2: Data Mining in a Nutshell

06:29

BADM 1.3: The Holdout Set

16:22

BADM 2.1: Data Visualization

12:25

BADM 2.2: Data Preparation

08:06

Regression 3: Other trend models (updated)

03:09

Prof Shmueli's Flipped Classroom

05:09

A Tree-Based Model for Addressing Self-Selection in Big Data: A Simple Explanation

01:06:47

To Explain or to Predict? - JMP Discovery Summit 2017 Plenary Session

01:03:54

JMP Analytically Speaking 2016 Featuring Galit Shmueli

13:30

Communication and Maintenance

07:15

Forecasting with Neural Networks: Part C

10:33

Forecasting with Neural Networks: Part B

11:48

Forecasting with Neural Networks: Part A

09:16

Forecasting with Logistic Regression (Part B)