Mathematical Methods of Data Theory

Lecture, three hours; discussion, one hour. Requisites: courses 42, 115A. Introduction to computational methods for data problems with focus on linear algebra and optimization. Matrix and tensor factorization, PageRank, assorted other topics in matrices, linear programming, unconstrained optimization, constrained optimization, integer optimization, dynamic programming, and stochastic optimization. P/NP or letter grading.

Review Summary

Clarity
1.7 / 10
Organization
3.3 / 10
Time
10-15 hrs/week
Overall
1.7 / 10

Reviews

    Quarter Taken: Winter 2023 In-Person
    Grade: A-

    Kassab's lectures were pretty dry and not too engaging. The content was also a mix of different topics, which was not only hard to follow but also difficult to see the usefulness of. Even the TA said that he did not know why this class existed. Looking back though, it did provide an introduction to some important concepts for later courses, like norms, and reinforce linear algebra topics, like matrix decomposition.

    One thing was that we were not given much on what to expect from the midterm or final, and there were no previous tests we could practice on. It ended up being mostly a regurgitation of lectures, including some minute details. Overall, it was not a fun class to be in, but I guess it did help me somewhat in later classes.

Course

Instructor
Kassab, L.
Previously taught
24W 23F 23W 22F

Grading Information

  • No group projects

  • Attendance not required

  • 1 midterm

  • Finals week final

  • 100% recommend the textbook

Previous Grades

Grade distributions not available.