Applied Machine Learning


4 units

Course Description

Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data.


INFO 206B, or equivalent course in Python programming; INFO 271B, or equivalent graduate-level course in statistics or econometrics; or permission of instructor.

Requirements Satisfied

MIMS: Technology Requirement
Ph.D. Breadth — Engineering and Design
Ph.D. Major/Minor Areas — Information Organization and Retrieval
Ph.D. Major/Minor Areas — Information Systems Design
Applied Data Science Certificate — Analytical Methods and Techniques of Data Science
Last updated: October 14, 2022