Bayesian Statistics and Computing, 2021 Spring L0: Course IntroductionR for Data ScienceL1: R basicsL2: Managing data frames with the dplyr packageL3: Control structures and functionsL4: Data visualization in RL5: Debugging and profiling R codeL6: Reproducible learning via rmarkdown (slides, html, pdf, word versions are available with source code available here)OptimizationL7: Newton’s method (R code: newton.R, newton.raphson.deriv.R, newton.raphson.D.R)L8: Quasi-Newton methodsL9: Derivative free methods (supplementary material)L10: Stochastic gradient descent (SGD) (supplementary material)Bayesian ComputingL11: Bayesian thinkingL12: Introduction to Bayesian computingL13: Independent Monte CarloL14: Markov Chain Monte Carlo (Hints for Lab11)Applied Linear AlgebraL15: Singular value decomposition (SVD)L16: Numerical algorithms for eigenanalysisL17: Image Recognition based on SVDReview
