Introduction to R from a software engineering perspective and an introduction to algorithms commonly used in applied statistics. Optimization algorithms including gradient descent, stochastic gradient descent, the EM algorithm, and topics in convex optimization. Stochastic algorithms including rejection sampling, Metropolis-Hastings, and Gibbs sampling.