Global and local optimization, constrained and unconstrained optimization, convex versus nonconvex optimization, Projection Theorem, linear search methods, gradient methods, Newton and Quasi-Newton methods, trust region methods.
4
UnitsOptional
Grading1
PasstimeNone
Level LimitLetters and science
Collegehe admits himself that he's not an expert on this class and isn't really good at explaining but makes up for it by giving out free homework scores. you only need a 60% on hw to get full credit, but can sometimes be a hassle. exams are pretty computation heavy. dont expect the exams to be a copy past of the practice, theyre not. anythings fair game
He could work on explaining the algorithms we learn in this class to make them easier to work with, but he's very aware of this & you can tell he tries really hard to be as clear as he can. Funny guy, very lenient grader here since this class was mainly algorithms. As long as he saw you were using them right, you'd get most credit. 10/10 very fair.
This prof would constantly forget to post important things on canvas which can be really annoying when you're trying really hard to get a good grade in the class. Homework is really hard and it just doesn't seem like he gaf. Somewhat funny but makes everything more complicated than they need to be
He's the one
He is GOAT
The game theory class taken with Karel is really fun, the concepts are explained in a way that's easier to understand, and he's also accessible during office hour (a lot of OH). Though sometimes he would forget to post some important stuff (like homework on gradescope, or lecture notes he promised), but the class is really good.
Paul Atzberger
2.6
Stephen Bigelow
3.4
Xiaolei Zhao
3.7
Therese Landry
2.9
XU Yang
2.8