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Aude Billard
École polytechnique fédérale de Lausanne (EPFL)
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Lecture 9 | Part 1 - Advanced Machine Learning Course
Bagging
16:13
Lecture 9 | Part 2 - Advanced Machine Learning Course
Boosting
25:03
Lecture 8 | Part 1 - Advanced Machine Learning Course
Brief review of C-SVM
35:40
Lecture 5 | Part 1
Classification - Introduction
4:30
Lecture 5 | Part 2
Classification with GMM
20:30
Lecture 3 | Part 1
Clustering Principle
11:08
Lecture 9 | Part 2
Comparison-GMR-SV
11:13
Lecture 7 | Part 3
Deep neural networks and more
32:41
Lecture 3 | Part 3
Evaluation for Clustering
21:09
Lecture 9 | Part 3 - Advanced Machine Learning Course
Features Selections
18:26
Lecture 4 | Part 3
Fitting and clustering data with Mixture of Gauss Functions
17:49
Lecture 4 | Part 2
Fitting data with one Gauss function
12:10
Lecture 9 | Part 1
Gaussian Mixture Regression
24:15
Lecture 12: Part - 1 - Advanced Machine Learning Course
HMM
39:06
Lecture 12 | Part - 2 - Advanced Machine Learning Course
HMM - Applications to robotics
26:37
Lecture 1 | Part 1
Introduction to Machine Learning
22:23
Lecture 6 - Advanced Machine Learning Course
Kernel K-means
50:15
Lecture 3 | Part 2-1
K-means
14:28
Lecture 5 | Part 3
KNN Classifier
11:24
Lecture 6 Part 2
Linear SVM derivation
17:00
Lecture 8 | Part 2
Linear & Weighted Regression
14:05
Lecture 5 | Part 4
Metrics for classification
31:55
Lecture 6 | Part 6
Multiclass SVM
5:44
Lecture 7 | Part 2
Neural Networks: Multi-layers
28:13
Lecture 7 | Part 1
Neural Networks: Perceptron
29:10
Lecture 6 Part 4
Nonlinear SVM
12:34
Lecture 8 | Part 2 - Advanced Machine Learning Course
Nu-SVM
9:42
Lecture 2 | Part 3
PCA - Derivation
15:33
Lecture 2 | Part 2
PCA - Intuition
7:53
Lecture 1 | Part 2
Pitfalls and Caveats in Machine Learning
17:19
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