Description: (Same as Computer Science M146.) Lecture, four hours; discussion, one hour; outside study, seven hours. Requisites: course 131A or Civil and Environmental Engineering 110 or Mathematics 170A or Statistics 100A, Computer Science 33. Introduction to breadth of data science. Foundations for modeling data sources, principles of operation of common tools for data analysis, and application of tools and models to data gathering and analysis. Topics include statistical foundations, regression, classification, kernel methods, clustering, expectation maximization, principal component analysis, decision theory, reinforcement learning and deep learning. Letter grading.

Units: 4.0