Bayesian Statistics and Computing L0: Course Introduction (Lab 1 reference) R for Data ScienceL1: R basicsL2: Managing data frames with the dplyr packageL3: Control structures and functionsL4: Data visualization in R L5: 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 method (newton.R, Student showcase 1, Student showcase 2) L8: Quasi-Newton methods L9: Derivative free methods (nmgif.R, Lab7.pdf, Lab7.html, supplementary material) L10: Stochastic gradient descent (SGD) (supplementary material, Lab8.pdf, Student showcase 1) Bayesian Computing L11: Bayesian thinking L12: Introduction to Bayesian computing L13: Independent Monte Carlo L14: Markov Chain Monte Carlo Applied Linear Algebra L15: Singular value decomposition (SVD) (Learning materials) L16: Numerical algorithms for eigenanalysis L17: Applications of SVD Review Final Report Requirements
