(Same as Electrical and Computer Engineering M146.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C; Civil and Environmental Engineering 110 or Electrical and Computer Engineering 131A or Mathematics 170A or 170E or Statistics 100A; Mathematics 33A. 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.

Review Summary

Clarity
8.3 / 10
Organization
8.3 / 10
Time
5-10 hrs/week
Overall
8.3 / 10

Reviews

    Quarter Taken: Fall 2022 In-Person
    Grade: A

    class is pretty cool, homework not too bad

Course

Previously taught
22F
Formerly offered as
COM SCI 188

Grading Information

  • No group projects

  • Attendance not required

  • 2 midterms

  • Finals week final

  • 0% recommend the textbook

Previous Grades

Grade distributions not available.