STAT 760 - Statistical Learning

(3 units)

Modern methods of supervised learning. Linear and polynomial regression; classification; model assessment and selection; model inference; simulation and re-sampling; neural networks; special topics.

Prerequisite(s): STAT 467 or STAT 667; STAT 445 or STAT 645 or STAT 745.

Grading Basis: Graded
Units of Lecture: 3
Offered: Every Spring

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. select appropriate statistical learning models listed in the course description for solving a variety of practical problems
2. 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.
3. perform discussed statistical analyses using a statistical package R, prepare reports, and present their results to a professional audience.

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