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
CollegeYu leaves much to be desired in their teaching approach. Classes often felt disorganized, with a lack of clear objectives or structured material. Key concepts were often rushed through or skipped entirely, making it challenging to understand the course content fully.
Best lecturer in pstat department.
I took this class as an undergrad. I found this class pretty interesting. The grading is 45% hw, 45% project, 10% lecture scribe. You're expected to use LaTeX in RMarkdown for homework and LaTeX for scribing (only one lecture per person.) The homework assignments are pretty challenging but you have plenty of time to do them cus they're only 3 of em
Class is easy for a graduate course, a lot of content overlaps with the stuff for PSTAT 131 students, except for the final that has a proofs section a little bit harder but still doable for a grad student. The final project's instructions were vague but my final project was dogsh*t and got an A- so he's chill. If u want easy A, take coburn instead
Avoid this guy if you can! He gave a super difficult final exam, with a lot of confusing multiple choice/true or false questions. He was difficult to reach outside of the class. His office hour didn't have anyone, including himself.
Very difficult to get a hold of outside of one or two days a week. Always rushing out and prefer to hide behind emails or zoom for communications. Unwilling to set up office hour outside of days he lectures. Don't feel like he cares about student's education, but cares more about his own time and schedule.