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VU Machine Learning 2018

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14 Review and outlook (MLVU2018)

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13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo (MLVU2018)

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12 Matrix Models: Recommender systems and Matrix factorization (MLVU2018)

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11 Models for Sequential Data: Markov Models, Word2Vec, RNNs and LSTMs.

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10 Tree Models and Ensembles: Decision Trees, Boosting, Bagging, Gradient Boosting (MLVU2018)

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09 Deep Learning 2: GANs, Variational Autoencoders (MLVU2018)

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08 Probabilistic Models 2, Normal Distributions, Gaussian Mixtures and EM (MLVU2018)

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07 Linear Models 2: Support Vector Machines, the Kernel trick (MLVU2018)

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06 Deep Learning 1: Neural networks, Convolutional layers (MLVU2018)

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05 Probabilistic Models 1: Naive Bayes, Entropy, Logistic Regression (MLVU2018)

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