Stats 102B Matrix Computation and Optimization for Statistics

Course description:
Introduction to matrix computation and optimization methods for statistical theory and applications. Main topics include
1) Vectors and vector spaces: inner products, norms, geometrical properties, variance and covariance of vectors;
2) Matrix computations: trace, determinant, linear operators, self-adjoint operators, quadratic forms, eigenanalysis;
3) Gradient-based optimization: gradient and the Hessian, stationary points, the Lagrangian, Newton's method;
4) Applications in statistics: multivariate normal distributions, principal components, singular value decomposition.

Lectures: MWF 1-1:50pm, PAB 1749.  Discussion session: Th 1-1:50pm, WGYoung 4216.
Instructor: Qing Zhou (zhou@stat.ucla.edu), Office Hour: Wed 4pm – 5:30pm, MS 8979.
TA: Ryan Rosario (rosario@stat.ucla.edu), Office Hour: Thu 12-1pm, MS 8105H.

Solutions:    Hw1    Hw2    Hw3    Hw4    Hw5    Hw6