Bayesian Statistics and Computing L0: Course Introduction (Lab 1) 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 L8: Quasi-Newton methods L9: Derivative free methods L10: Stochastic gradient descent (SGD) 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) L16: Numerical algorithms for eigenanalysis L17: Applications of SVD Review
