Statistical Machine Learning is used to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression tress, 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
PasstimeNone
Level LimitLetters and science
CollegeAvoid 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.
Class is graded on Midterm/Take Home Final/4 HW/4 Quiz. Midterm content was fair and was primarily lecture material. Midterm had multiple choice and free response. Homework was reasonable and was based off lab assignment topics and code. You could work with a partner on the homework and the final. Overall good instructor. would take again
Lectures are solid and he really tries his best to simplify down complex ideas to make them understandable. Homework is more like follow along learn as you go rather than challenging to complete, and really helps with understanding models. Lectures can be a little math heavy but he doesn't expect you to memorize, instead focuses on concepts
Hes so awful omg don't take him
His first time teaching. He's not understanding and gives you less time to do work when he says he will give you 2 weeks