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
CollegeI just self-studied for all the materials and got an A. Exams become easy if you can 100% solve all these practice problems.
The instructor was largely unresponsive to emails, including inquiries about the final exam. Even the TA had difficulty reaching her. Lectures were minimal and lacked depth, requiring significant self-study to truly understand the material. If you're looking for a course with strong guidance and engagement, this might not be the best choice.
Good professor with knowledge of the subject. Straightforward in their expectations and lectures.
The 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.