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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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