CSE40: 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.

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

General Education Code SR

5 credits

Year	Fall	Winter	Spring	Summer
2025-26	Section 01 Alex Rudnick (ajrudnic) Section 02 Hao Yue (hyue11)	Section 02 Hao Yue (hyue11) Section 01 Cihang Xie (cixie)	Section 01 Jeffrey M Flanigan (jmflanig)
2024-25	Section 01 Lise Getoor (getoor)	Section 01 Alex Rudnick (ajrudnic)	Section 01 Niloofar Montazeri (nimontaz)	Section 01 Staff
2023-24	Section 01 Lise Getoor (getoor)	Section 01 Lise Getoor (getoor)	Section 01 Yang Liu (yangliu)
2022-23		Section 01 Lise Getoor (getoor)

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