Bayesian Statistics and Computing, 2020 Spring L0: Course IntroductionR for Data Science L1: R basicsL2: Managing data frames with the dplyr packageL3: Control structures and functions (Hints for Lab1-Lab3)L4: Data visualization in RL5: Debugging and profiling R code L6: Reproducible learning via rmarkdown (slides, html, pdf, word versions are available with source code available here)Optimization L7: Newton’s methodL8: Quasi-Newton methodsL9: Derivative free methods (supplementary material)L10: Stochastic gradient descent (SGD) (Hints for Lab8)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 eigenanalysisReview
