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Aude Billard
École polytechnique fédérale de Lausanne (EPFL)
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Lecture 2 - Kernel PCA, Advanced Machine Learning | Lecture 2 - 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 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
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