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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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