STAT 202A: Statistical Programming

The purpose of this course is to teach the art of statistical programming in R, Python, and C++, by writing computer code to implement the following core algorithms in statistical computing.

  • Least squares regression, sweep operator, QR decomposition.
  • Eigen computation, Principal Component Analysis.
  • Logistic regression, Newton-Raphson.
  • Feed-forward neural network, back-propagation.
  • Support vector machine, primal and dual solvers.
  • Adaboost, coordinate descent.
  • Ridge regression, spline.
  • Lasso, stagewise regression, solution path.
  • Factor analysis, EM.
  • Random number generators, linear congruential, rejection, polar.
  • MCMC, Metropolis, Langevin, Gibbs sampler

    When going through the above topics, the focus will be on algorithms and especially programming, instead of theories of learning, inference and computing.

    We shall also touch on important issues such as data sets, graphics, packages, etc.