Selective Topics on Optimization
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	- - - - - - - - - - - - Part VIII Machine Learning Online Course by Stanford Lecture 1 Lecture 2 Lecture 3 Lecture 4 Lecture 5 Lecture 6 Lecture 7 Lecture 8 Lecture 9 Lecture 10 Lecture 11 Lecture 12 Lecture 13 Lecture 14 Lecture 15 Lecture 16 Lecture 17 Lecture 18 Lecture 19 Lecture 20 Bayesian Nonparametrics Lecture 1 Lecture 2 Lecture 3 Deep learning NIPS 2014 Workshop: Deep Learning and Representation Learning, by Geoff Hinton Recent Developments in Deep Learning, by Geoff Hinton Introduction to Deep Learning & Deep Belief Nets, by Geoff Hinton Machine Learning Course by University of Oxford Lecture 1: Introduction, Video, slides Lecture 2: Linear prediction, Video, slides Lecture 3: Maximum likelihood, Video, slides Lecture 4: Regularizers, basis functions and cross-validation, Video, slides Lecture 5: Optimisation, Video, slides Lecture 6: Logistic regression, Video, slides Lecture 7: Back-propagation and layer-wise design of neural nets, Video, slides Lecture 8: Neural networks and deep learning with Torch, Video, slides Lecture 9: Convolutional neural networks, Video, slides Lecture 10: Max-margin learning and siamese networks, Video, slides Lecture 11: Recurrent neural networks and LSTMs, Video, slides Lecture 12: Reinforcement learning, slides Multi-Way Analysis Tensor Decomposition Theory and Algorithms in the Era of Big Data, by Dr Nikos Sidiropoulos - - - - - - - - - - - - Introduction - Convex Optimization - Nonlinear Optimization - Stochastic Programming Dynamic Optimization- Game Theory - Integer Programming- Sparse Optimization- Machine Learning