Course @ Ecole polytechnique by Erwan Le Pennec and Francoise d'Alché-Buc
Large review of machine learning principles
SVM - SVR, theory of RKHS, supervised and unsupervised learning in a RKHS
Unsupervised learning, dimension reduction
Tree & ensemble methods: decision/regression trees, ensemble (bagging, random forest, adaboost, gradient boosting, anyboost)
Graphs in ML I: spectral clustering, transductive learning, semi-supervised learning
Graphs in ML II: KNN for collaborative filtering, matrix factorization (with link with PCA, convex formulation & proximal gradient descent), applications to image completion
Kernels and margin bounds: review on kernels, margin theory
Introduction to Neural Networks: perceptron, multlayer perceptron, backpropagation algorithm
Feature design (renormalization, dictionnary learning, feature encoding, quantization/binarization, hashing, pooling) and text/image mining (texts and bag of words, word vectors, convolutional networks, recurrent neural networks for text)