Aude Billard

Lecture 8 | Part 3 Support Vector Regression (SVR)

26:05

Lecture 8 | Part 2 Linear & Weighted Regression

14:05

Lecture 8 | Part 1 Regression Introduction

6:09

Lecture 7 | Part 2 Neural Networks: Multi-layers

28:13

Lecture 7 | Part 3 Deep neural networks and more

32:41

Lecture 7 | Part 1 Neural Networks: Perceptron

29:10

Lecture 6 | Part 7 Pros & Cons of SVM

11:27

Lecture 6 | Part 6 Multiclass SVM

5:44

Lecture 6 Part 5 SVM Hyperparameters

3:45

Lecture 6 Part 4 Nonlinear SVM

12:34

Lecture 6 Part 3 SVM for Non-separable datasets

3:07

Lecture 6 Part 2 Linear SVM derivation

17:00

Lecture 6 Part 1 SVM - Principle

7:12

Lecture 5 | Part 4 Metrics for classification

31:55

Lecture 5 | Part 2 Classification with GMM

20:30

Lecture 5 | Part 3 KNN Classifier

11:24

Lecture 5 | Part 1 Classification - Introduction

4:30

Lecture 4 | Part 4 Probabilistic Interpr. of K-Means & GMM

19:14

Lecture 4 | Part 1 Recap of Probabilities and Densities (optional)

254:23:23

Lecture 4 | Part 2 Fitting data with one Gauss function

12:10

Lecture 4 | Part 3 Fitting and clustering data with Mixture of Gauss Functions

17:49

Lecture 3 | Part 3 Evaluation for Clustering

21:09

Lecture 3 | Part 2 -2 Soft K-means & DBSCAN

17:02

Lecture 3 | Part 1 Clustering Principle

11:08

Lecture 3 | Part 2-1 K-means

14:28

Lecture 2 | Part 3 PCA - Derivation

15:33

Lecture 2 | Part 2 PCA - Intuition

7:53

Lecture 2 | Part 1 Principal Component Analysis - Principle

11:21

Lecture 1 | Part 2 Pitfalls and Caveats in Machine Learning

17:19

Lecture 1 | Part 1 Introduction to Machine Learning

22:23