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
CollegeShe 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.
I took Coburn for 120A my junior year and didn't love her as I felt like she didn't teach us enough. This course, on the other hand, she teaches very well. It's a lot of fun as 50% of the class is working on a final project and the rest is homeworks that help you with that project. She's genuinely fantastic at teaching this class.
She is a fantastic professor in all aspects. My only qualm with her is her unresponsiveness over email, but this is mostly because I don't go to class :)
I got an A according to my assignment grades + my Gauchospace grade and on GOLD I have a B. I reached out to Coburn about this but she's highly unresponsive so I reached out to my TA TWICE as well. My grade is STILL wrong and probably won't ever be fixed. I just graduated so like whatever but this isn't an issue students should have to deal with.
Love Coburn so much. I took 120B and 131 with her. She's the best professor in the stats department period!
50% homework and 50% final project, very clear about the grading critera and very very generous about grades. Super nice prof and an easy A for basically all classes she teaches. Never responds to emails though, so go to office hours/lecture for questions. Great professor and I learned a lot.