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Bayesian Methods for Machine Learning
Introduction to Bayesian methods
001. Think bayesian & Statistics review (7:26)
002. Bayesian approach to statistics (5:25)
003. How to define a model (3:22)
004. Example thief & alarm (11:10)
005. Linear regression (10:49)
Conjugate Priors
006. Analytical inference (3:50)
007. Conjugate distributions (2:59)
008. Example Normal, precision (5:34)
009. Example Bernoulli (4:30)
Latent Variable Models
010. Latent Variable Models (11:32)
011. Probabilistic clustering (6:32)
012. Gaussian Mixture Model (10:14)
013. Training GMM (10:41)
014. Example of GMM training (10:35)
Expectation Maximization Algorithm
015. Jensen's inequality & Kullback Leibler divergence (9:40)
016. Expectation-Maximization algorithm (10:50)
017. E-step details (12:21)
018. M-step details (6:34)
019. Example EM for discrete mixture, E-step (10:23)
020. Example EM for discrete mixture, M-step (12:17)
021. Summary of Expectation Maximization (6:46)
Applications and examples
022. General EM for GMM (12:37)
023. K-means from probabilistic perspective (9:44)
024. K-means, M-step (7:06)
025. Probabilistic PCA (13:07)
026. EM for Probabilistic PCA (7:24)
Variational Inference
027. Why approximate inference (5:15)
028. Mean field approximation (13:59)
029. Example Ising model (15:39)
030. Variational EM & Review (5:52)
Latent Dirichlet Allocation
031. Topic modeling (5:21)
032. Dirichlet distribution (6:44)
033. Latent Dirichlet Allocation (5:57)
034. LDA E-step, theta (11:46)
035. LDA E-step, z (8:50)
036. LDA M-step & prediction (13:22)
037. Extensions of LDA (5:09)
MCMC
038. Monte Carlo estimation (12:46)
039. Sampling from 1-d distributions (13:29)
040. Markov Chains (13:07)
041. Gibbs sampling (12:31)
042. Example of Gibbs sampling (7:54)
043. Metropolis-Hastings (8:17)
044. Metropolis-Hastings choosing the critic (8:43)
045. Example of Metropolis-Hastings (9:56)
046. Markov Chain Monte Carlo summary (8:50)
047. MCMC for LDA (15:22)
048. Bayesian Neural Networks (11:05)
Variational Autoencoders
049. Scaling Variational Inference & Unbiased estimates (6:25)
050. Modeling a distribution of images (10:32)
051. Using CNNs with a mixture of Gaussians (8:00)
052. Scaling variational EM (15:08)
053. Gradient of decoder (6:16)
054. Log derivative trick (6:43)
055. Reparameterization trick (7:58)
Variational Dropout
056. Learning with priors (5:50)
057. Dropout as Bayesian procedure (5:57)
058. Sparse variational dropout (5:42)
Gaussian Processes and Bayesian Optimization
059. Nonparametric methods (6:02)
060. Gaussian processes (8:04)
061. GP for machine learning (5:35)
062. Derivation of main formula (11:19)
063. Nuances of GP (12:11)
064. Bayesian optimization (10:15)
065. Applications of Bayesian optimization (5:05)
023. K-means from probabilistic perspective
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