[FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning

Creation Time	May 19, 2024, 7:27 a.m.
Last Access Time	Nov. 7, 2024, 5:47 a.m.
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007.Latent Dirichlet Allocation/036. LDA M-step & prediction.mp4 93.5 MB
006.Variational inference/028. Mean field approximation.mp4 77.3 MB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.mp4 75.6 MB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.mp4 69.9 MB
006.Variational inference/029. Example Ising model.mp4 68.2 MB
004.Expectation Maximization algorithm/017. E-step details.mp4 66.2 MB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.mp4 65.5 MB
005.Applications and examples/022. General EM for GMM.mp4 62.5 MB
008.MCMC/041. Gibbs sampling.mp4 61.4 MB
001.Introduction to Bayesian methods/004. Example thief & alarm.mp4 59.8 MB
007.Latent Dirichlet Allocation/035. LDA E-step, z.mp4 59.2 MB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.mp4 56.4 MB
001.Introduction to Bayesian methods/005. Linear regression.mp4 50.1 MB
009.Variational autoencoders/052. Scaling variational EM.mp4 47.8 MB
008.MCMC/040. Markov Chains.mp4 47.1 MB
008.MCMC/039. Sampling from 1-d distributions.mp4 47.0 MB
008.MCMC/047. MCMC for LDA.mp4 46.7 MB
008.MCMC/038. Monte Carlo estimation.mp4 44.5 MB
008.MCMC/044. Metropolis-Hastings choosing the critic.mp4 42.0 MB
005.Applications and examples/025. Probabilistic PCA.mp4 39.0 MB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.mp4 36.8 MB
003.Latent Variable Models/010. Latent Variable Models.mp4 36.8 MB
008.MCMC/045. Example of Metropolis-Hastings.mp4 36.6 MB
010.Variational Dropout/057. Dropout as Bayesian procedure.mp4 35.0 MB
008.MCMC/048. Bayesian Neural Networks.mp4 34.0 MB
009.Variational autoencoders/050. Modeling a distribution of images.mp4 32.2 MB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.mp4 32.0 MB
003.Latent Variable Models/013. Training GMM.mp4 31.6 MB
003.Latent Variable Models/014. Example of GMM training.mp4 31.3 MB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.mp4 31.2 MB
005.Applications and examples/024. K-means, M-step.mp4 31.0 MB
010.Variational Dropout/056. Learning with priors.mp4 30.4 MB
008.MCMC/043. Metropolis-Hastings.mp4 29.9 MB
010.Variational Dropout/058. Sparse variational dropout.mp4 29.6 MB
003.Latent Variable Models/012. Gaussian Mixture Model.mp4 29.2 MB
005.Applications and examples/023. K-means from probabilistic perspective.mp4 28.5 MB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.mp4 28.4 MB
008.MCMC/042. Example of Gibbs sampling.mp4 27.6 MB
008.MCMC/046. Markov Chain Monte Carlo summary.mp4 26.8 MB
009.Variational autoencoders/055. Reparameterization trick.mp4 25.2 MB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.mp4 24.9 MB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.mp4 24.2 MB
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.mp4 23.7 MB
005.Applications and examples/026. EM for Probabilistic PCA.mp4 21.8 MB
003.Latent Variable Models/011. Probabilistic clustering.mp4 21.7 MB
009.Variational autoencoders/054. Log derivative trick.mp4 20.8 MB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.mp4 20.5 MB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.mp4 20.3 MB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.mp4 19.5 MB
009.Variational autoencoders/053. Gradient of decoder.mp4 19.3 MB
004.Expectation Maximization algorithm/018. M-step details.mp4 19.2 MB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.mp4 18.2 MB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.mp4 18.2 MB
006.Variational inference/030. Variational EM & Review.mp4 17.4 MB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.mp4 17.1 MB
007.Latent Dirichlet Allocation/031. Topic modeling.mp4 16.8 MB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.mp4 16.6 MB
002.Conjugate priors/008. Example Normal, precision.mp4 16.4 MB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.mp4 16.4 MB
007.Latent Dirichlet Allocation/037. Extensions of LDA.mp4 15.8 MB
006.Variational inference/027. Why approximate inference.mp4 15.7 MB
002.Conjugate priors/009. Example Bernoulli.mp4 14.0 MB
002.Conjugate priors/006. Analytical inference.mp4 13.8 MB
001.Introduction to Bayesian methods/003. How to define a model.mp4 10.0 MB
002.Conjugate priors/007. Conjugate distributions.mp4 9.2 MB
008.MCMC/047. MCMC for LDA.srt 20.8 KB
009.Variational autoencoders/052. Scaling variational EM.srt 18.9 KB
008.MCMC/038. Monte Carlo estimation.srt 16.9 KB
006.Variational inference/029. Example Ising model.srt 16.9 KB
008.MCMC/039. Sampling from 1-d distributions.srt 16.5 KB
005.Applications and examples/025. Probabilistic PCA.srt 16.0 KB
008.MCMC/040. Markov Chains.srt 15.7 KB
003.Latent Variable Models/010. Latent Variable Models.srt 15.1 KB
008.MCMC/048. Bayesian Neural Networks.srt 14.8 KB
005.Applications and examples/022. General EM for GMM.srt 14.2 KB
009.Variational autoencoders/050. Modeling a distribution of images.srt 14.2 KB
011.Gaussian Processes and Bayesian Optimization/063. Nuances of GP.srt 13.8 KB
003.Latent Variable Models/013. Training GMM.srt 13.7 KB
004.Expectation Maximization algorithm/016. Expectation-Maximization algorithm.srt 13.4 KB
003.Latent Variable Models/014. Example of GMM training.srt 13.1 KB
004.Expectation Maximization algorithm/017. E-step details.srt 13.0 KB
003.Latent Variable Models/012. Gaussian Mixture Model.srt 12.9 KB
008.MCMC/041. Gibbs sampling.srt 12.9 KB
001.Introduction to Bayesian methods/004. Example thief & alarm.srt 12.5 KB
011.Gaussian Processes and Bayesian Optimization/064. Bayesian optimization.srt 12.5 KB
008.MCMC/045. Example of Metropolis-Hastings.srt 12.5 KB
008.MCMC/046. Markov Chain Monte Carlo summary.srt 12.4 KB
004.Expectation Maximization algorithm/020. Example EM for discrete mixture, M-step.srt 12.4 KB
004.Expectation Maximization algorithm/015. Jensen's inequality & Kullback Leibler divergence.srt 11.9 KB
006.Variational inference/028. Mean field approximation.srt 11.7 KB
007.Latent Dirichlet Allocation/036. LDA M-step & prediction.srt 11.6 KB
001.Introduction to Bayesian methods/005. Linear regression.srt 11.2 KB
005.Applications and examples/023. K-means from probabilistic perspective.srt 11.2 KB
001.Introduction to Bayesian methods/001. Think bayesian & Statistics review.srt 10.6 KB
004.Expectation Maximization algorithm/019. Example EM for discrete mixture, E-step.srt 10.1 KB
008.MCMC/043. Metropolis-Hastings.srt 9.7 KB
009.Variational autoencoders/051. Using CNNs with a mixture of Gaussians.srt 9.7 KB
011.Gaussian Processes and Bayesian Optimization/060. Gaussian processes.srt 9.6 KB
011.Gaussian Processes and Bayesian Optimization/062. Derivation of main formula.srt 9.5 KB
007.Latent Dirichlet Allocation/034. LDA E-step, theta.srt 9.4 KB
009.Variational autoencoders/055. Reparameterization trick.srt 9.4 KB
008.MCMC/042. Example of Gibbs sampling.srt 9.3 KB
008.MCMC/044. Metropolis-Hastings choosing the critic.srt 9.2 KB
010.Variational Dropout/056. Learning with priors.srt 8.7 KB
005.Applications and examples/026. EM for Probabilistic PCA.srt 8.7 KB
010.Variational Dropout/057. Dropout as Bayesian procedure.srt 8.3 KB
009.Variational autoencoders/049. Scaling Variational Inference & Unbiased estimates.srt 8.3 KB
007.Latent Dirichlet Allocation/032. Dirichlet distribution.srt 8.2 KB
004.Expectation Maximization algorithm/021. Summary of Expectation Maximization.srt 8.1 KB
003.Latent Variable Models/011. Probabilistic clustering.srt 8.0 KB
004.Expectation Maximization algorithm/018. M-step details.srt 8.0 KB
009.Variational autoencoders/054. Log derivative trick.srt 8.0 KB
009.Variational autoencoders/053. Gradient of decoder.srt 7.6 KB
006.Variational inference/030. Variational EM & Review.srt 7.6 KB
010.Variational Dropout/058. Sparse variational dropout.srt 7.5 KB
011.Gaussian Processes and Bayesian Optimization/059. Nonparametric methods.srt 7.5 KB
007.Latent Dirichlet Allocation/035. LDA E-step, z.srt 7.5 KB
005.Applications and examples/024. K-means, M-step.srt 7.2 KB
001.Introduction to Bayesian methods/002. Bayesian approach to statistics.srt 6.9 KB
002.Conjugate priors/008. Example Normal, precision.srt 6.7 KB
007.Latent Dirichlet Allocation/033. Latent Dirichlet Allocation.srt 6.6 KB
007.Latent Dirichlet Allocation/031. Topic modeling.srt 6.6 KB
011.Gaussian Processes and Bayesian Optimization/061. GP for machine learning.srt 6.4 KB
006.Variational inference/027. Why approximate inference.srt 6.3 KB
007.Latent Dirichlet Allocation/037. Extensions of LDA.srt 6.2 KB
011.Gaussian Processes and Bayesian Optimization/065. Applications of Bayesian optimization.srt 6.1 KB
002.Conjugate priors/009. Example Bernoulli.srt 5.4 KB
002.Conjugate priors/006. Analytical inference.srt 4.9 KB
001.Introduction to Bayesian methods/003. How to define a model.srt 4.1 KB
002.Conjugate priors/007. Conjugate distributions.srt 3.4 KB
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[FreeCoursesOnline.Me] Coursera - Bayesian Methods for Machine Learning

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