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[Coursera] Bayesian Methods for Machine Learning - [FCO] TGx Exclusive

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File	Size
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4	23.69 MB
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt	10.61 kB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4	17.07 MB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt	6.93 kB
001.Introduction to Bayesian methods/003. How to define a model.mp4	10.05 MB
001.Introduction to Bayesian methods/003. How to define a model.srt	4.14 kB
001.Introduction to Bayesian methods/004. Example thief & alarm.mp4	59.85 MB
001.Introduction to Bayesian methods/004. Example thief & alarm.srt	12.53 kB
001.Introduction to Bayesian methods/005. Linear regression.mp4	50.06 MB
001.Introduction to Bayesian methods/005. Linear regression.srt	11.24 kB
002.Conjugate priors/006. Analytical inference.mp4	13.82 MB
002.Conjugate priors/006. Analytical inference.srt	4.86 kB
002.Conjugate priors/007. Conjugate distributions.mp4	9.22 MB
002.Conjugate priors/007. Conjugate distributions.srt	3.37 kB
002.Conjugate priors/008. Example Normal, precision.mp4	16.41 MB
002.Conjugate priors/008. Example Normal, precision.srt	6.72 kB
002.Conjugate priors/009. Example Bernoulli.mp4	14.02 MB
002.Conjugate priors/009. Example Bernoulli.srt	5.44 kB
003.Latent Variable Models/010. Latent Variable Models.mp4	36.78 MB
003.Latent Variable Models/010. Latent Variable Models.srt	15.14 kB
003.Latent Variable Models/011. Probabilistic clustering.mp4	21.70 MB
003.Latent Variable Models/011. Probabilistic clustering.srt	8.04 kB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4	29.16 MB
003.Latent Variable Models/012. Gaussian Mixture Model.srt	12.90 kB
003.Latent Variable Models/013. Training GMM.mp4	31.61 MB
003.Latent Variable Models/013. Training GMM.srt	13.74 kB
003.Latent Variable Models/014. Example of GMM training.mp4	31.27 MB
003.Latent Variable Models/014. Example of GMM training.srt	13.15 kB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4	28.36 MB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt	11.87 kB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4	31.97 MB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt	13.37 kB
004.Expectation Maximization algorithm/017. E-step details.mp4	66.24 MB
004.Expectation Maximization algorithm/017. E-step details.srt	12.96 kB
004.Expectation Maximization algorithm/018. M-step details.mp4	19.21 MB
004.Expectation Maximization algorithm/018. M-step details.srt	8.00 kB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4	56.37 MB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt	10.13 kB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4	65.47 MB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt	12.37 kB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4	20.29 MB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt	8.07 kB
005.Applications and examples/022. General EM for GMM.mp4	62.53 MB
005.Applications and examples/022. General EM for GMM.srt	14.24 kB
005.Applications and examples/023. K-means from probabilistic perspective.mp4	28.46 MB
005.Applications and examples/023. K-means from probabilistic perspective.srt	11.20 kB
005.Applications and examples/024. K-means, M-step.mp4	30.95 MB
005.Applications and examples/024. K-means, M-step.srt	7.18 kB
005.Applications and examples/025. Probabilistic PCA.mp4	38.98 MB
005.Applications and examples/025. Probabilistic PCA.srt	16.02 kB
005.Applications and examples/026. EM for Probabilistic PCA.mp4	21.80 MB
005.Applications and examples/026. EM for Probabilistic PCA.srt	8.67 kB
006.Variational inference/027. Why approximate inference.mp4	15.74 MB
006.Variational inference/027. Why approximate inference.srt	6.28 kB
006.Variational inference/028. Mean field approximation.mp4	77.30 MB
006.Variational inference/028. Mean field approximation.srt	11.66 kB
006.Variational inference/029. Example Ising model.mp4	68.23 MB
006.Variational inference/029. Example Ising model.srt	16.86 kB
006.Variational inference/030. Variational EM & Review.mp4	17.38 MB
006.Variational inference/030. Variational EM & Review.srt	7.58 kB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4	16.76 MB
007.Latent Dirichlet Allocation/031. Topic modeling.srt	6.59 kB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4	20.49 MB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt	8.17 kB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4	18.22 MB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt	6.65 kB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4	75.56 MB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt	9.42 kB
007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4	59.22 MB
007.Latent Dirichlet Allocation/035. LDA E-step, z.srt	7.48 kB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4	93.47 MB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt	11.63 kB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4	15.83 MB
007.Latent Dirichlet Allocation/037. Extensions of LDA.srt	6.17 kB
008.MCMC/038. Monte Carlo estimation.mp4	44.51 MB
008.MCMC/038. Monte Carlo estimation.srt	16.89 kB
008.MCMC/039. Sampling from 1-d distributions.mp4	47.05 MB
008.MCMC/039. Sampling from 1-d distributions.srt	16.47 kB
008.MCMC/040. Markov Chains.mp4	47.06 MB
008.MCMC/040. Markov Chains.srt	15.71 kB
008.MCMC/041. Gibbs sampling.mp4	61.41 MB
008.MCMC/041. Gibbs sampling.srt	12.88 kB
008.MCMC/042. Example of Gibbs sampling.mp4	27.59 MB
008.MCMC/042. Example of Gibbs sampling.srt	9.29 kB
008.MCMC/043. Metropolis-Hastings.mp4	29.90 MB
008.MCMC/043. Metropolis-Hastings.srt	9.74 kB
008.MCMC/044. Metropolis-Hastings choosing the critic.mp4	42.01 MB
008.MCMC/044. Metropolis-Hastings choosing the critic.srt	9.19 kB
008.MCMC/045. Example of Metropolis-Hastings.mp4	36.61 MB
008.MCMC/045. Example of Metropolis-Hastings.srt	12.47 kB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4	26.83 MB
008.MCMC/046. Markov Chain Monte Carlo summary.srt	12.37 kB
008.MCMC/047. MCMC for LDA.mp4	46.68 MB
008.MCMC/047. MCMC for LDA.srt	20.83 kB
008.MCMC/048. Bayesian Neural Networks.mp4	34.03 MB
008.MCMC/048. Bayesian Neural Networks.srt	14.81 kB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4	19.50 MB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt	8.25 kB
009.Variational autoencoders/050. Modeling a distribution of images.mp4	32.24 MB
009.Variational autoencoders/050. Modeling a distribution of images.srt	14.23 kB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4	24.85 MB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt	9.70 kB
009.Variational autoencoders/052. Scaling variational EM.mp4	47.78 MB
009.Variational autoencoders/052. Scaling variational EM.srt	18.92 kB
009.Variational autoencoders/053. Gradient of decoder.mp4	19.31 MB
009.Variational autoencoders/053. Gradient of decoder.srt	7.63 kB
009.Variational autoencoders/054. Log derivative trick.mp4	20.79 MB
009.Variational autoencoders/054. Log derivative trick.srt	7.98 kB
009.Variational autoencoders/055. Reparameterization trick.mp4	25.18 MB
009.Variational autoencoders/055. Reparameterization trick.srt	9.37 kB
010.Variational Dropout/056. Learning with priors.mp4	30.39 MB
010.Variational Dropout/056. Learning with priors.srt	8.72 kB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4	35.03 MB
010.Variational Dropout/057. Dropout as Bayesian procedure.srt	8.34 kB
010.Variational Dropout/058. Sparse variational dropout.mp4	29.61 MB
010.Variational Dropout/058. Sparse variational dropout.srt	7.50 kB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4	18.16 MB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt	7.49 kB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4	24.18 MB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt	9.63 kB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4	16.36 MB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt	6.41 kB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4	69.86 MB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt	9.46 kB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4	36.81 MB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt	13.79 kB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4	31.23 MB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt	12.53 kB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4	16.61 MB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt	6.06 kB
Discuss.FreeTutorials.Us.html	165.68 kB
FreeCoursesOnline.Me.html	108.30 kB
FreeTutorials.Eu.html	102.23 kB
How you can help Team-FTU.txt	259.00 B
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Description

Instructors: Daniil Polykovskiy, Alexander Novikov
Offered By: National Research University Higher School of Economics
Language: English
Subtitle: Included
Torrent Contains: 136 Files, 11 Folders
Course Source: https://www.coursera.org/learn/bayesian-methods-in-machine-learning

About this Course

Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a really desirable feature for fields like medicine.
When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money.
In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can fully automate this workflow and how to speed it up using some advanced techniques.
We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.

About National Research University Higher School of Economics

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more. Learn more on www.hse.ru

SKILLS YOU WILL GAIN

• Bayesian Optimization
• Gaussian Process
• Markov Chain Monte Carlo (MCMC)
• Variational Bayesian Methods

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