Statistical Learning I

4 credits

Bayesian decision theory; parameter estimation; maximum likelihood; the bias-variance trade-off; Bayesian estimation; the predictive distribution; conjugate and noninformative priors; dimensionality and dimensionality reduction; principal component analysis; Fisher’s linear discriminant analysis; density estimation; parametric vs. kernel-based methods; expectation-maximization; applications. Recommended preparation: ECE 109. Prerequisites: graduate standing.