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Aude Billard
École polytechnique fédérale de Lausanne (EPFL)
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Lecture 1 | Part 2, Pitfalls and Caveats in Machine Learning
17:19
Lecture 12 | Part 1 (Advanced Machine Learning), Linear and kernel CCA
28:18
Lecture 8 | Part 3, Ridge Regression
11:44
Lecture 19 | Part 1 (Lecture 10 - Advanced Machine Learning), Discrete Reinforcement Learning
38:21
Lecture 18 | Part 1 (Lec 9 - Advanced Machine Learning), Gaussian Process Regression
26:28
Lecture 17 | Part 2 - (Lec 7) Advanced Machine Learning, SVR Extensions: Nu-SVR and RVR
21:05
Lecture 14 | (Lec 4) Advanced Machine Learning, Spectral Clustering and Laplacian Eigenmaps
26:53
Lecture 13 | (Lec 3) Advanced Machine Learning, Kernel K-means
21:49
Lecture 11 | (Lec 2) Advanced Machine Learning, Kernel PCA
26:01
Lecture 9 | Part 2, Comparison-GMR-SV
11:13
Lecture 9 | Part 1, Gaussian Mixture Regression
24:15
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 7, SVM Summary
5:27
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 6 | Part 3, Quantifying performance
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
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