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

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

    14:50 |
    Download MP3

    Related Videos

    (ML 19.1) Gaussian processes - definition and first examples 12:06

    (ML 19.1) Gaussian processes - definition and first examples

    (ML 14.1) Markov models - motivating examples 13:29

    (ML 14.1) Markov models - motivating examples

    (ML 16.6) Gaussian mixture model (Mixture of Gaussians) 14:51

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

    (ML 4.1) Maximum Likelihood Estimation (MLE) (part 1) 14:47

    (ML 4.1) Maximum Likelihood Estimation (MLE) (part 1)

    (PP 1.1) Measure theory: Why measure theory - The Banach-Tarski Paradox 8:38

    (PP 1.1) Measure theory: Why measure theory - The Banach-Tarski Paradox

    (ML 16.3) Expectation-Maximization (EM) algorithm 14:37

    (ML 16.3) Expectation-Maximization (EM) algorithm

    (ML 2.5) Generalizations for trees (CART) 13:20

    (ML 2.5) Generalizations for trees (CART)

    (PP 6.1) Multivariate Gaussian - definition 14:23

    (PP 6.1) Multivariate Gaussian - definition

    (ML 15.1) Newton's method (for optimization) - intuition 11:16

    (ML 15.1) Newton's method (for optimization) - intuition

    (ML 18.1) Markov chain Monte Carlo (MCMC) introduction 17:04

    (ML 18.1) Markov chain Monte Carlo (MCMC) introduction

    (ML 17.3) Monte Carlo approximation 14:51

    (ML 17.3) Monte Carlo approximation

    (ML 6.1) Maximum a posteriori (MAP) estimation 13:31

    (ML 6.1) Maximum a posteriori (MAP) estimation

    (ML 10.3) Posterior for linear regression (part 2) 14:55

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

    (ML 14.4) Hidden Markov models (HMMs) (part 1) 14:30

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

    (ML 16.1) K-means clustering (part 1) 13:33

    (ML 16.1) K-means clustering (part 1)

    (PP 6.9) Conditional distributions of a Gaussian 14:57

    (PP 6.9) Conditional distributions of a Gaussian

    (ML 2.6) Bootstrap aggregation (Bagging) 14:57

    (ML 2.6) Bootstrap aggregation (Bagging)

    (ML 17.5) Importance sampling - introduction 13:43

    (ML 17.5) Importance sampling - introduction

    (PP 3.1) Random Variables - Definition and CDF 13:40

    (PP 3.1) Random Variables - Definition and CDF

    (IC 4.1) Huffman coding - introduction and example 12:31

    (IC 4.1) Huffman coding - introduction and example