ECE 271AStatistical Learning I
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
From the catalog: “graduate standing.”
Courses that list ECE 271A as a prerequisite (7)
- BENG 285 Statistical Learning in Bioinformatics
- BNFO 285 Statistical Learning in Bioinformatics
- ECE 204 Statistical Learning in Bioinformatics
- ECE 209 Statistical Learning for Biosignal Processing
- ECE 226 Optimization and Acceleration of Deep Learning on Various Hardware Platforms
- ECE 271B Statistical Learning II
- ECE 271C Deep Learning and Applications