Machine Learning & Data Mining

STA325

The rapid growth of digitalized data and the computer power available to analyze it has created immense opportunities for both machine learning and data mining. This course introduces machine learning and data mining methods. Topics covered include information retrieval, clustering, classification, modern regression, cross validation, boosting and bagging. Course emphasizes selection of appropriate methods and justification of choice, use of programming for implementation of the method, and evaluation and effective communication of results in data analysis reports. Prerequisite: Statistical Science 210, 230, and 250 (Statistical Science 250 may be taken concurrently). Recommended prerequisite: Statistical Science 323. Instructor: Steorts

Curriculum Codes: 

QS