Computer Science and EngineeringCSE 40 Machine Learning Basics: Data Analysis and Empirical Methods

Introduction to the basic mathematical concepts and programming abstractions required for modern machine learning, data science, and empirical science. The mathematical foundations include basic probability, linear algebra, and optimization. The programming abstractions include data manipulation and visualization. The principles of empirical analysis, evaluation, critique and reproducibility are emphasized. Mathematical and programming abstractions are grounded in empirical studies including data-driven evidential reasoning, predictive modeling, and causal analysis.

Requirements

Prerequisite(s): MATH 19B or MATH 20B, and CSE 30.

Credits

5

General Education Code

SR

Quarter offered

Spring

Instructor

Lise Getoor, The Staff