Overview
Description
Modern methods of supervised learning. Linear and polynomial regression; classification; model assessment and selection; model inference; simulation and re-sampling; neural networks; special topics.
Requirements
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Attributes
Standard
Learning Outcomes
- select appropriate statistical learning models listed in the course description for solving a variety of practical problems
- perform theoretical and numerical model analysis (including estimation of model parameters, assessing the estimator variability, performing statistical tests) and interpret the results in terms of the examined applied problem.
- perform discussed statistical analyses using a statistical package R, prepare reports, and present their results to a professional audience.