Info 251

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.

Prerequisites

Info 206 or equivalent college-level course in computer science in Python; Info 271B or equivalent graduate-level coursework in statistics or econometrics.

Last updated:

April 7, 2017