29:24

Academia vs Industry: A Twelve Point Comparison

45:30

Machine Learning Class: Neural Networks: Part II

01:02:18

Machine Learning Class: Neural Networks: Part I

01:01:02

Machine Learning Class: Gaussian Mixture Models and EM

01:05:18

Machine Learning Class: Ensembles, Bagging and Boosting: Part II

01:15:02

Machine Learning Class: Ensembles, Bagging and Boosting: Part I

01:02:00

Machine Learning Class: Bias Variance Tradeoff

01:08:15

Machine Learning Class: VC Dimension: Part II

24:14

Machine Learning Class: VC Dimension: Part I

46:36

Machine Learning Class: Computational Learning Theory: Part II

21:50

Machine Learning Class: Computational Learning Theory: Part I

01:06:12

Machine Learning Class: Clustering

01:13:34

Machine Learning Class: Logistic Regression

46:39

Machine Learning Class: Naïve Bayes: Part II

55:12

Machine Learning Class: Naïve Bayes: Part I

50:42

Machine Learning Class: Bayesian Methods: Part II

29:45

Machine Learning Class: Bayesian Methods: Part I

02:02:25

Optimization in Machine Learning (Lecture 11): Applications

33:22

Optimization in Machine Learning (Lecture 10): Difference of Submodular Opt, SCSC & SCSK

01:46:43

Optimization in Machine Learning (Lecture 9):Submodular Maximization and Greedy

02:23:36

Optimization in Machine Learning (Lecture 8): Polyhedra, Extensions, and Submodular Minimization

01:32:32

Optimization in Machine Learning (Lecture 7 Continued): Submodular Information Measures

02:30:36

Optimization in Machine Learning: Lec 7 (Submodular Functions: Definitions, Examples, Properties)

01:22:55

Optimization in Machine Learning: Lecture 6.2 (SGD for Deep Learning)

01:18:52

Optimization in Machine Learning: Lecture 6.1 (Stochastic Gradient Descent)

02:34:35

Optimization in Machine Learning: Lecture 5 (Second Order Methods and Coordinate Descent)

02:30:28

Optimization in Machine Learning: Lec 4 (Conditional GD and Projected GD, Quasi-Newton, BFGS etc.)

02:42:43

Optimization in Machine Learning: Lec 3 (Gradient Descent Cont., Nesterov's GD, Proximal GD, Demos)

02:54:50

Optimization in Machine Learning: Lecture 2 (Convex Functions Cont, Analysis of Gradient Descent)

02:37:47

Optimization in Machine Learning: Lecture 1 (Outline, Logistics, Convexity)

02:04:15

IJCAI 2020 Tutorial Part II: Submodular Optimization for Data, Feature, and Topic Summarization.

01:34:32

IJCAI 2020 Tutorial Part I: Submodular Optimization for Data, Feature, and Topic Summarization.

28:33

Machine Learning Class: Nearest Neighbor Classifiers and k-d Trees

01:02:02

Machine Learning Class: Decision Trees

42:56

Machine Learning Class: Support Vector Machines and Slack

22:37

Machine Learning Class: Lagrange Multipliers and Duality of SVMs (Part 2)

32:35

Machine Learning Class: Lagrange Multipliers and Duality of SVMs (Part 1)

26:28

Machine Learning Class: Intro to SVMs (Part 2) + Demo on SVMs

33:51

Machine Learning Class: Intro to SVMs (Part 1)

11:24

Machine Learning Class: Perceptron (Part 4: Demo)

12:50

Robust Submodular Minimization with Applications to Cooperative Modeling (ECAI 2020 Talk)

30:22

Submodular Information Measures (SPCOM 2020 Invited Talk)

17:30

Machine Learning Class: Perceptron (Part 3)

22:34

Machine Learning Class: Perceptron (Part 2: Optimization and Sub-Gradient Descent)

31:35

Machine Learning Class: Perceptrons (Part 1)

23:42

Machine Learning Class: Regression (Part 4: Hands On)

12:40

Machine Learning Class: Regression (Part 3: Polynomial Regression)

22:10

Machine Learning Class: Regression (Part 2: Gradient Descent and Optimization)

17:40

Machine Learning Class: Regression (Part 1: Linear Regression Formulation)

37:10

Machine Learning Class: Introduction to ML (Part 4: Loss Functions and Evaluation)

30:15

Machine Learning Class: Introduction to ML (Part 3: Examples)

14:54

Machine Learning Class: Introduction to ML (Part 2: Types of ML)

17:46

Machine Learning Class: Introduction to ML (Part 1: Basics)