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
CollegeThe lectures were lackluster and behind schedule, making the course feel a lot harder than it really was. Coburn grades generously, but I feel that I've learned very little in her class.
Frequently made mistakes on example problems and often did not give clear answers to student questions. Resulting slow pace led to us missing 2 full weeks of content. Didn't really promote understanding of material, just how to plug in formulas. Honestly not even worth the easy A for PSTAT majors. Take someone else if possible
Dr. Coburn is a really sweet lady and posts all her lecture videos online for 120C. The exams are tough and you won't find the answers in hw or anything, but doable if you watch all the lecture videos in entirety. Pay close attention to hw because it is graded with scrutiny. She is super caring and helpful in office hours so definitely go.
confusing lectures, doesnt teach and doesnt respond to emails. the midterm was hard (way too hard for undergrad class) and you dont really learn anything so if youre a stats major pls dont take her
Her lectures were pretty incomprehensible and hard to follow. She will literally never respond to emails and TAs can't get a hold of her. Homework and quizzes were graded on completion which helped your grade but did not help you learn. The midterm was extremely difficult that she had to make the final exactly like the review and midterm.
She was really organised for this class and fair and it was pretty easy to do well. 5 homeworks - one due every two weeks worth 10% each, and you were allowed submit up to two homeworks up to a week late with no penalty. Final project worth 50% which you could do on whatever and she tells you all about that right at the start.