The lecture slides will be released right before the class.
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The lecture slides will be released right before the class.
| Date | Topic | Reading Materials | Assignment |
|---|---|---|---|
| Tue 8.20 | Class Overview: Probabilistic Machine Leanring Definition, Course Topics [Video] | ||
| Thu 8.22 | Class Overview: Course website, Convex Functions[Annotated][Video] | ||
| Tue 8.27 | Probability: Probability Space, Mean, Variance, CDF, PDF, Joint Dist., Condition Dist. [Annotated][Video] | ||
| Thu 8.29 | Probability distributions: MLE, MAP, Binomial, Multinomial, Beta, Dirichlet, Gausisan, Student t [Video] | ||
| Tue 9.03 | Information theory: Entropy, Differential Entropy, KL diveregence, multual information; [Annotated]Conjugate priors:Beta [Video] | ||
| Thu 9.05 | Conjugate priors:Beta, Gamma, Dirichlet, Wishart, exponential family and properties [Annotated] [Video] | ||
| Tue 9.10 | Noninformative priors:Bayesian Philosophy, uniform, translation invaraince[Annotated] [Video] | ||
| Thu 9.12 | Noninformative priors:Jeffery's prior, exchangeability, De Finetti's theorem [Annotated] [Video] | ||
| Tue 9.17 | Bayesian Decision Theorem:Decision Region, Minimum Classification Rate [Annotated-1] Generalized Linear Models:Linear Regression, Regularizer [Annotated-2] [Video] | ||
| Thu 9.19 | Generalized Linear Models:Design matrix, MLE, Bayesian Linear Regression, Regularization, Predictive Distribution, Evidence Maximization, Empirical Bayes, Type II MLE [Video] | ||
| Tue 9.24 | Generalized Linear Models:Logistic regression, multi-class, probit regression, ordinal regression, generalized linear models [Video] | ||
| Thu 9.26 | Probabilistic Graphical Models:Bayesian Networks, Conditional Indepdence, D-separation [Annotated] [Video] | ||
| Tue 10.01 | Probabilistic Graphical Models:Markov Blanket, Markov Random Fields, Moralization [Annotated] Inference: Forward and Backward messages [Annotated] [Video] | ||
| Thu 10.03 | Probabilistic Graphical Model Inference:factor-graphs, sum-product [Annotated] [Video] | ||
| Tue 10.08 | |||
| Thu 10.10 | |||
| Tue 10.15 | Probabilistic Graphical Model Inference: Max-Product, Max-Sum [Annotated] [Video] | ||
| Thu 10.17 | Detailed Examples of Max-Sum [Annotated], Approximate Inference Laplace's Approximation: General Ideas [Annotated] [Video] | ||
| Tue 10.22 | Laplace Approximation: Bayesian Logistc Regression [Annotated] Variational Inference: Gaussian Mixture Model and EM Algorithm [Annotated] [Video] | ||
| Thu 10.24 | Variational Inference: Global Variational Inference [Annotated] [Video] | ||
| Tue 10.29 | Variational Inference: Local Variational Inference, variational logistic regression [Annotated] [notes-sigmoid] | ||
| Thu 10.31 | Variational Inference: Variational Message Passing Latent Dirichlet Allocation:Model, Variational Inference [Video] | ||
| Tue 11.05 | Linear Algebra and Matrix Derivative Review [Video] | ||
| Thu 11.07 | Markov Chain Monte Carlo Sampling: Markov Chains, Invariant/Stationary Dist., Transition Kernel, Ergodicity, Detailed Balance, Metroplis-Hasting Algorihthm [Annotated][Video] | ||
| Tue 11.12 | Markov Chain Monte Carlo Sampling: Gibbs Sampling, Matrix Factorization, Proof of Correctness [Annotated][Video] | ||
| Thu 11.14 | TensorFlow: Tutorial [Sample Code][Video] | ||
| Tue 11.19 | Hybird Monte Carlo Sampling: Hamiltonian System, Reversability, Conservation, Volume Preservation, Leapfrog, Correctness [Annotated][Video] | ||
| Thu 11.21 | Bayesian Neural Networks: Neural Networks, Back Propagation, Bayesian NNs, Reparameterization trick [Video] | ||
| Tue 11.25 | Bayesian Neural Networks: Reparameterization trick, Bayes by BP, Auto-Encoding Variational Bayes, GANs [Video] | ||
| Thu 11.27 | |||
| Tue 12.03 | Bayesian Neural Networks: GANs, Mini-Max Optimization Gaussian process regression: GP Prior, Random Process, Kernel Function, Linear Model View[Annotated][Video] | ||
| Thu 12.05 | Gaussian process regression: connection to Bayesian NN Final Review |