Statistical and machine learning approaches to computational uncertainty quantification in mathematical models with applications to computer simulations, images, and time-series, spatio-temporal, and functional data. Topics include computer model emulation and design, reproducing kernel Hilbert spaces, Gaussian processes, dynamic systems, the Kalman filter, inverse problems, and Bayesian optimization.
4
UnitsOptional
Grading1, 2, 3
PasstimeGraduate students only
Level LimitLetters and science
CollegeThis course was taken during COVID, but his online class was not hard. There were no midterms, and the coding projects that replaced that were relatively easy. Largely, you just copied and altered his sample R code. The homework was pretty heavy but was not graded harshly. Overall, he was a nice guy and solid choice for online learning.
He was overwhelming. You will feel that too until you get your final grade. He curves the final grade insanely. People who got around 70% on the midterm and below 50% on the final got A- and B+. So don't freak out too much. He is a nice guy.
(NEVER) TAKE HIM! QUESTION ANSWERED. PLEASE (AVOID) TAKING HIM!
Fantastic Professor! He encourages you to self-study by not teaching. For cheaters, he is awesome! Take his class and enjoy watching your tuition disappearing!
I don't know why people talk bad things about him. He deserves worse than that! Take his class if you have a super smart friend who can sit next to you in the midterm and provide you all the solutions because he is nice enough to let you do that!
If your goal is to drop interest in statistics, then go for his class. By far the worst guy I've ever taken. Super unorganized lecture, unfriendly and disrespectful to students, not caring students at all.