Aude Billard

Lecture 16 | Part 1 - (Lec 6) Advanced Machine Learning , Bagging

Lecture 16 | Part 2 - (Lec 6) Advanced Machine Learning , Boosting

Lecture 16 | Part 1 - Advanced Machine Learning Course, Brief review of C-SVM

Lecture 5 | Part 1, Classification - Introduction

Lecture 5 | Part 2, Classification with GMM

Lecture 3 | Part 1, Clustering Principle

Lecture 9 | Part 2, Comparison-GMR-SV

Lecture 7 | Part 3, Deep neural networks and more

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

Lecture 3 | Part 3, Evaluation for Clustering

Lecture 16 | Part 3 - (Lec 6) Advanced Machine Learning , Features Selections

Lecture 4 | Part 3, Fitting and clustering data with Mixture of Gauss Functions

Lecture 4 | Part 2, Fitting data with one Gauss function

Lecture 9 | Part 1, Gaussian Mixture Regression

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

Lecture 20 | Part 1 - (Lec 10) Advanced Machine Learning Course, Hidden Markov Models (HMM) - Theory

Lecture 20 | Part2 - (Lec 10) Advanced Machine Learning Course, HMM - Applications to robotics

Lecture 1 | Part 1, Introduction to Machine Learning

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

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

Lecture 3 | Part 2-1, K-means

Lecture 5 | Part 3, KNN Classifier

Lecture 13 - Live lecture in 2020, Lecture 13 - Kernel K-means

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

Lecture 6 Part 2, Linear SVM derivation

Lecture 8 | Part 2 , Linear & Weighted Regression

Lecture 5 | Part 4, Metrics for classification

Lecture 6 | Part 6, Multiclass SVM

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

Lecture 7 | Part 1, Neural Networks: Perceptron