Stats 102B Computation and Optimization for Statistics
Course
description:
Introduction to computational methods and optimization useful for statisticians.
Use of computer programming to solve statistical problems. Main topics include:
1) Matrix Algebra: vector and matrix computation, connections to statistics, eigenvalue decomposition.
2) Principal Component Analysis: multivariate normal distribution, principal components,
dimension reduction, PC regression.
3) Clustering Analysis: distance matrices, hierarchical clustering, K-mean clustering.
4) Gradient-based Optimization: gradient and Hessian, Newton's method, Fisher scoring.
5) EM Algorithm: missing data, the EM algorithm, examples.
Link to the course Moodle site for lecture notes, homework assignments, etc.