Explores computationally-intensive methods in statistics. Topics covered include Fundamentals of Optimization, Combinatorial Optimization, EM algorithm, Monte Carlo simulation, Markov Chain Monte Carlo methods. Lab work is carried out using R or Python.
4
UnitsOptional
Grading1, 2, 3
PasstimeGraduate students only
Level LimitLetters and science
CollegeYu leaves much to be desired in their teaching approach. Classes often felt disorganized, with a lack of clear objectives or structured material. Key concepts were often rushed through or skipped entirely, making it challenging to understand the course content fully.
Best lecturer in pstat department.
I took this class as an undergrad. I found this class pretty interesting. The grading is 45% hw, 45% project, 10% lecture scribe. You're expected to use LaTeX in RMarkdown for homework and LaTeX for scribing (only one lecture per person.) The homework assignments are pretty challenging but you have plenty of time to do them cus they're only 3 of em
Class is easy for a graduate course, a lot of content overlaps with the stuff for PSTAT 131 students, except for the final that has a proofs section a little bit harder but still doable for a grad student. The final project's instructions were vague but my final project was dogsh*t and got an A- so he's chill. If u want easy A, take coburn instead
Terrible professor. Lectures are disorganized and confusing, and he rarely answers questions clearly. He reads straight from the slides without explaining concepts. Grading is inconsistent, and there's no feedback on assignments. Office hours feel rushed and unhelpful. I learned more from YouTube than from class.
Don't know what's up with the negative reviews. I agree that lectures were focused on theoretical concepts it's math course so what do you expect? He knows the material inside out, with well-structured homeworks that actually help you understand the content. Final exam was fair, it's just that without a midterm we didn't know what was expected.