Statistical Machine Learning is used to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression trees, random forests, clustering and association rules. Building predictive models focusing on model selection, model comparison and performance evaluation. Emphasis will be on concepts, methods and data analysis; and students are expected to complete a significant class project, individual or team based, using real world data.
4
UnitsOptional
Grading1, 2, 3
PasstimeNone
Level LimitLetters and science
Collegetook 120c online over the summer. overall good class, attendance was mandatory which sucked but otherwise fine
131 with coburn was probably one of the best classes i've had at UCSB. you can tell she really cares about the material and how passionate she is. the project was also super useful
Coburn is very sweet and understanding. I will say sometimes the lectures can sometimes be hard to follow, but things like her posting handwritten notes I found quite helpful. Tests/quizzes aren't too bad
A mix of semi-lackluster lectures plus a bad TA (got unlucky, my TA was very dull and hard to understand) made me learn so little in this class, self-studying was much more effective. However, she's likely your best bet for the 120 series given decent tests and caring/kind attitude, even if she can be difficult to reach occasionally.
Didn't learn much from lectures, but easy tests and grading scheme made up for it. Overall would recommend taking.
Nice person but not very accessible outside of class. Lectures are lackluster, expect to self study a big portion of the class. Dont expect to leave with good knowledge of mathematical statistics.
Andrew Carter
3.1
Alexander Franks
4.8
Wendy Meiring
2.8
Jean-Pierre Fouque
4.0
Mengyang Michael Gu
3.8