Aude Billard

Lecture 1 | Part 2, Pitfalls and Caveats in Machine Learning

Lecture 12 | Part 1 (Advanced Machine Learning), Linear and kernel CCA

Lecture 8 | Part 3, Ridge Regression

Lecture 19 | Part 1 (Lecture 10 - Advanced Machine Learning), Discrete Reinforcement Learning

Lecture 18 | Part 1 (Lec 9 - Advanced Machine Learning), Gaussian Process Regression

Lecture 17 | Part 2 - (Lec 7) Advanced Machine Learning, SVR Extensions: Nu-SVR and RVR

Lecture 14 | (Lec 4) Advanced Machine Learning, Spectral Clustering and Laplacian Eigenmaps

Lecture 13 | (Lec 3) Advanced Machine Learning, Kernel K-means

Lecture 11 | (Lec 2) Advanced Machine Learning, Kernel PCA

Lecture 9 | Part 2, Comparison-GMR-SV

Lecture 9 | Part 1, Gaussian Mixture Regression

Lecture 8 | Part 3, Support Vector Regression (SVR)

Lecture 8 | Part 2 , Linear & Weighted Regression

Lecture 8 | Part 1, Regression Introduction

Lecture 7 | Part 2, Neural Networks: Multi-layers

Lecture 7 | Part 3, Deep neural networks and more

Lecture 7 | Part 1, Neural Networks: Perceptron

Lecture 6 | Part 7, Pros & Cons of SVM

Lecture 6 | Part 6, Multiclass SVM

Lecture 6 | Part 7, SVM Summary

Lecture 6 Part 5 , SVM Hyperparameters

Lecture 6 Part 4, Nonlinear SVM

Lecture 6 Part 3, SVM for Non-separable datasets

Lecture 6 Part 2, Linear SVM derivation

Lecture 6 Part 1, SVM - Principle

Lecture 5 | Part 4, Metrics for classification

Lecture 6 | Part 3, Quantifying performance

Lecture 5 | Part 2, Classification with GMM

Lecture 5 | Part 3, KNN Classifier

Lecture 5 | Part 1, Classification - Introduction