Computational statistics is the interface between statistics and computing. We will cover algorithms for solving statistical problems for model fitting, prediction/generalization, and uncertainty quantification. We will talk about how to use optimization and other modern computational techniques to develop algorithms for various types of data. Topics include linear and non-linear regression, splines, density estimation and kernel methods, Gaussian mixture models and EM algorithm, hidden Markov models, Monte Carlo Markov chain (MCMC), and bootstrap.