Statistical Computing, 2019 spring

Lecture notes

• L0: Course Introduction
• R for Data Science
  • L1: R basics
  • L2: Managing data frames with the dplyr package
  • L3: Control structures and functions
  • L4: Dealing with text data
  • L5: Debugging and profiling R code
• Optimization
  • L6: Newton’s method
  • L7: Quasi-Newton methods
  • L8: Derivative free methods
• Computational Linear Algebra
  • L9: Eigenanalysis
  • L10: Singular value decomposition (SVD)
  • L11: Basic applications of SVD
  • L12: Numerical algorithms for eigenanalysis
  • L13: Image recognition based on SVD
  • L14: SVD in text mining
• L15: Review