Syllabus and Policies
Table of contents
Prerequisites
We expect students to have completed a course in introductory probability theory (e.g. EECS 126 or equivalent), and some other upper-division or graduate course involving mathematical proof and argumentation (e.g. EE 227a, EE 221, Math 104).
A background in real analysis and linear systems theory is a plus, but not strictly required.
Recommended Texts
This course will follow a set of lecture notes written by Professor Thomas Courtade, which will be made available through bCourses.
Students seeking to review real analysis material are encouraged to pick up Principles of Mathematical Analysis by Walter Rudin.
Grading
The breakdown for final grading will be as follows:
- 10% Homework
- 5% Participation
- 40% Midterm
- 45% Final
Exams
The midterm will be a take-home exam on March 5. The final will be held in-person during the university-prescribed hours on May 8, 11:30-2:30pm.
Homework and Collaboration
We encourage discussion of homework problems with your classmates, but you should write up your final solutions on your own. You may reference external sources and textbooks to aid you in your solutions, but you must credit all collaborators and resources used in your homework submission.
Homeworks will be self-graded, and you may recover 50% of any points lost by submitting revised homework solutions. For final grade calculation, we will scale your homework scores (after appropriate resubmission credit has been applied) so that 80% counts for full credit.