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NIPS 2016 - Generative Adversarial Networks - Ian Goodfellow

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(ML 1.1) Machine learning - overview and application

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(ML 17.11) Rejection sampling - uniform case

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(ML 13.5) Generative process specification

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(ML 1.4) Variations on supervised and unsupervised

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(ML 14.4) Hidden Markov models (HMMs) (part 1)

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(ML 17.2) Monte Carlo methods - A little history

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(ML 16.13) EM for MAP estimation

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(ML 14.2) Markov chains (discrete-time) (part 1)

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14:48

(ML 18.2) Ergodic theorem for Markov chains

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(ML 15.6) Logistic regression (binary) - computing the Hessian-jUwjbiBUR

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(ML 7.3) Proportionality

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(ML 16.6) Gaussian mixture model (Mixture of Gaussians)

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(ML 18.5) Examples of Markov chains with various properties (part 2)-daY4lgEyEPc.mkv

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(ML 13.7) Graphical model for Bayesian Naive Bayes-w6siVHrf2Ho.mkv

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(ML 13.1) Directed graphical models - introductory examples (part 1)-3XysEf3IQN4.mkv

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(ML 2.7) Bagging for classification-JM4Y0B6Ho90.mkv

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(ML 14.10) Underflow and the log-sum-exp trick--RVM21Voo7Q.mkv

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(ML 7.7.A1) Dirichlet distribution-nfBNOWv1pgE.mp4

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(ML 10.3) Posterior for linear regression (part 2)

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(ML 16.1) K-means clustering (part 1)-0MQEt10e4NM.mp4

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(ML 10.5) Predictive distribution for linear regression (part 2)-vTcsacTqlfQ.mkv

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(ML 4.2) Maximum Likelihood Estimation (MLE) (part 2)-2vkm5ZIRsJU.mp4

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(ML 7.7.A2) Expectation of a Dirichlet random variable-emnfq4txDuI.mkv

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(ML 5.2) Exponential families (part 2)-Jc-gVN8tQC8.mp4

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(ML 1.5) Generative vs discriminative models-oTtow2Ui8vg.mp4

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(ML 12.2) Bias-variance in model selection-W0NLs-A6hhQ.mkv

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(ML 18.4) Examples of Markov chains with various properties (part 1)-Pce7KKeUf5w.mkv

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(ML 15.3) Logistic regression (binary) - intuition--Z2a_mzl9LM.mp4

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(ML 19.1) Gaussian processes - definition and first examples-vU6AiEYED9E.mkv

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(ML 19.6) Inner products and PSD kernels-juTjfwTZ7XQ.mkv

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14:49

(ML 17.8) Smirnov transform (Inverse transform sampling) - invertible case-irheiVXJRm8.mkv

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