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
PasstimeGraduate students only
Level LimitLetters and science
CollegeDr. Coburn was great in lectures, and made the concepts seem simple to understand, but the ease of homework, examples and quiz questions left me improperly prepared for the midterm and final question styles. Very accesible, and office hours are helpful. Weekly homework and quizzes, a midterm and a final exam.
The professor is calm and cool. The exams are a bit hard and I would recommend studying with lots of practice problems weeks before exams. I found that the practice exams were not as helpful as the lecture examples.
honestly 120A was fine if you studied and showed up to class. it was hard but idk who expects it to be easy
All my friends said that he was the easy professor for 120A, but his practice tests were NOTHING like the actual exams. Lectures were boring and not useful. Fully made me lose the passion that I had for statistics. Ended up taking this class P/NP and dropped my stats minor. None of the content is interesting or applicable to the real world.
Test materials are different from lectures. He said he would not test the materials he had taught in the last several minutes, yet he still gave one.
Studied hours a week and still nearly failed. The student's effort doesn't pay off because the tests were not taught properly in the lectures either.