发帖
雷达卡
Course @ Ecole polytechnique by Erwan Le Pennec and Francoise d'Alché-Buc
Course 1Large review of machine learning principles
Course 2SVM - SVR, theory of RKHS, supervised and unsupervised learning in a RKHS
Course 3Unsupervised learning, dimension reduction
Course 4Tree & ensemble methods: decision/regression trees, ensemble (bagging, random forest, adaboost, gradient boosting, anyboost)
Course 5Graphs in ML I: spectral clustering, transductive learning, semi-supervised learning
Course 6Graphs in ML II: KNN for collaborative filtering, matrix factorization (with link with PCA, convex formulation & proximal gradient descent), applications to image completion
Course 7Kernels and margin bounds: review on kernels, margin theory
Course 8Introduction to Neural Networks: perceptron, multlayer perceptron, backpropagation algorithm
Course 9Feature 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)
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