[COURSERA] BAYESIAN METHODS FOR MACHINE LEARNING [FCO]

Code:

• 001. Think bayesian & Statistics review.mp4 (23.7 MB)
• 001. Think bayesian & Statistics review.srt (10.6 KB)
• 002. Bayesian approach to statistics.mp4 (17.1 MB)
• 002. Bayesian approach to statistics.srt (6.9 KB)
• 003. How to define a model.mp4 (10.0 MB)
• 003. How to define a model.srt (4.1 KB)
• 004. Example thief & alarm.mp4 (59.8 MB)
• 004. Example thief & alarm.srt (12.5 KB)
• 005. Linear regression.mp4 (50.1 MB)
• 005. Linear regression.srt (11.2 KB)
• 006. Analytical inference.mp4 (13.8 MB)
• 006. Analytical inference.srt (4.9 KB)
• 007. Conjugate distributions.mp4 (9.2 MB)
• 007. Conjugate distributions.srt (3.4 KB)
• 008. Example Normal, precision.mp4 (16.4 MB)
• 008. Example Normal, precision.srt (6.7 KB)
• 009. Example Bernoulli.mp4 (14.0 MB)
• 009. Example Bernoulli.srt (5.4 KB)
• 010. Latent Variable Models.mp4 (36.8 MB)
• 010. Latent Variable Models.srt (15.1 KB)
• 011. Probabilistic clustering.mp4 (21.7 MB)
• 011. Probabilistic clustering.srt (8.0 KB)
• 012. Gaussian Mixture Model.mp4 (29.2 MB)
• 012. Gaussian Mixture Model.srt (12.9 KB)
• 013. Training GMM.mp4 (31.6 MB)
• 013. Training GMM.srt (13.7 KB)
• 014. Example of GMM training.mp4 (31.3 MB)
• 014. Example of GMM training.srt (13.1 KB)
• 015. Jensen's inequality & Kullback Leibler divergence.mp4 (28.4 MB)
• 015. Jensen's inequality & Kullback Leibler divergence.srt (11.9 KB)
• 016. Expectation-Maximization algorithm.mp4 (32.0 MB)
• 016. Expectation-Maximization algorithm.srt (13.4 KB)
• 017. E-step details.mp4 (66.2 MB)
• 017. E-step details.srt (13.0 KB)
• 018. M-step details.mp4 (19.2 MB)
• 018. M-step details.srt (8.0 KB)
• 019. Example EM for discrete mixture, E-step.mp4 (56.4 MB)
• 019. Example EM for discrete mixture, E-step.srt (10.1 KB)
• 020. Example EM for discrete mixture, M-step.mp4 (65.5 MB)
• 020. Example EM for discrete mixture, M-step.srt (12.4 KB)
• 021. Summary of Expectation Maximization.mp4 (20.3 MB)
• 021. Summary of Expectation Maximization.srt (8.1 KB)
• 022. General EM for GMM.mp4 (62.5 MB)
• 022. General EM for GMM.srt (14.2 KB)
• 023. K-means from probabilistic perspective.mp4 (28.5 MB)
• 023. K-means from probabilistic perspective.srt (11.2 KB)
• 024. K-means, M-step.mp4 (31.0 MB)
• 024. K-means, M-step.srt (7.2 KB)
• 025. Probabilistic PCA.mp4 (39.0 MB)
• 025. Probabilistic PCA.srt (16.0 KB)
• 026. EM for Probabilistic PCA.mp4 (21.8 MB)
• 026. EM for Probabilistic PCA.srt (8.7 KB)
• 027. Why approximate inference.mp4 (15.7 MB)
• 027. Why approximate inference.srt (6.3 KB)
• 028. Mean field approximation.mp4 (77.3 MB)
• 028. Mean field approximation.srt (11.7 KB)
• 029. Example Ising model.mp4 (68.2 MB)
• 029. Example Ising model.srt (16.9 KB)
• 030. Variational EM & Review.mp4 (17.4 MB)
• 030. Variational EM & Review.srt (7.6 KB)
• 031. Topic modeling.mp4 (16.8 MB)
• 031. Topic modeling.srt (6.6 KB)
• 032. Dirichlet distribution.mp4 (20.5 MB)
• 032. Dirichlet distribution.srt (8.2 KB)
• 033. Latent Dirichlet Allocation.mp4 (18.2 MB)
• 033. Latent Dirichlet Allocation.srt (6.6 KB)
• 034. LDA E-step, theta.mp4 (75.6 MB)
• 034. LDA E-step, theta.srt (9.4 KB)
• 035. LDA E-step, z.mp4 (59.2 MB)
• 035. LDA E-step, z.srt (7.5 KB)
• 036. LDA M-step & prediction.mp4 (93.5 MB)
• 036. LDA M-step & prediction.srt (11.6 KB)
• 037. Extensions of LDA.mp4 (15.8 MB)
• 037. Extensions of LDA.srt (6.2 KB)
• 038. Monte Carlo estimation.mp4 (44.5 MB)
• 038. Monte Carlo estimation.srt (16.9 KB)
• 039. Sampling from 1-d distributions.mp4 (47.0 MB)
• 039. Sampling from 1-d distributions.srt (16.5 KB)
• 040. Markov Chains.mp4 (47.1 MB)
• 040. Markov Chains.srt (15.7 KB)
• 041. Gibbs sampling.mp4 (61.4 MB)
• 041. Gibbs sampling.srt (12.9 KB)
• 042. Example of Gibbs sampling.mp4 (27.6 MB)
• 042. Example of Gibbs sampling.srt (9.3 KB)
• 043. Metropolis-Hastings.mp4 (29.9 MB)
• 043. Metropolis-Hastings.srt (9.7 KB)
• 044. Metropolis-Hastings choosing the critic.mp4 (42.0 MB)
• 044. Metropolis-Hastings choosing the critic.srt (9.2 KB)
• 045. Example of Metropolis-Hastings.mp4 (36.6 MB)
• 045. Example of Metropolis-Hastings.srt (12.5 KB)
• 046. Markov Chain Monte Carlo summary.mp4 (26.8 MB)
• 046. Markov Chain Monte Carlo summary.srt (12.4 KB)
• 047. MCMC for LDA.mp4 (46.7 MB)
• 047. MCMC for LDA.srt (20.8 KB)
• 048. Bayesian Neural Networks.mp4 (34.0 MB)
• 048. Bayesian Neural Networks.srt (14.8 KB)
• 049. Scaling Variational Inference & Unbiased estimates.mp4 (19.5 MB)
• 049. Scaling Variational Inference & Unbiased estimates.srt (8.3 KB)
• 050. Modeling a distribution of images.mp4 (32.2 MB)
• 050. Modeling a distribution of images.srt (14.2 KB)
• 051. Using CNNs with a mixture of Gaussians.mp4 (24.9 MB)
• 051. Using CNNs with a mixture of Gaussians.srt (9.7 KB)
• 052. Scaling variational EM.mp4 (47.8 MB)
• 052. Scaling variational EM.srt (18.9 KB)
• 053. Gradient of decoder.mp4 (19.3 MB)
• 053. Gradient of decoder.srt (7.6 KB)
• 054. Log derivative trick.mp4 (20.8 MB)
• 054. Log derivative trick.srt (8.0 KB)
• 055. Reparameterization trick.mp4 (25.2 MB)
• 055. Reparameterization trick.srt (9.4 KB)
• 056. Learning with priors.mp4 (30.4 MB)
• 056. Learning with priors.srt (8.7 KB)
• 057. Dropout as Bayesian procedure.mp4 (35.0 MB)
• 057. Dropout as Bayesian procedure.srt (8.3 KB)
• 058. Sparse variational dropout.mp4 (29.6 MB)
• 058. Sparse variational dropout.srt (7.5 KB)
• 059. Nonparametric methods.mp4 (18.2 MB)
• 059. Nonparametric methods.srt (7.5 KB)
• 060. Gaussian processes.mp4 (24.2 MB)
• 060. Gaussian processes.srt (9.6 KB)
• 061. GP for machine learning.mp4 (16.4 MB)
• 061. GP for machine learning.srt (6.4 KB)
• 062. Derivation of main formula.mp4 (69.9 MB)
• 062. Derivation of main formula.srt (9.5 KB)
• 063. Nuances of GP.mp4 (36.8 MB)
• 063. Nuances of GP.srt (13.8 KB)
• 064. Bayesian optimization.mp4 (31.2 MB)
• 064. Bayesian optimization.srt (12.5 KB)
• 065. Applications of Bayesian optimization.mp4 (16.6 MB)
• 065. Applications of Bayesian optimization.srt (6.1 KB)
• [FreeCoursesOnline.Me].url (0.1 KB)
• [FreeTutorials.Us].url (0.1 KB)
• [FTU Forum].url (0.2 KB)