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's a good professor with just homeworks, section attendance, and a 50% final project. The project covers all the material from throughout the course and takes about 10-15 hours to finish. The grading is very fair, follow the rubric and you'll get an A!
131 with coburn was probably one of the best classes i've had at UCSB. you can tell she really cares about the material and how passionate she is. the project was also super useful
Good professor with knowledge of the subject. Straightforward in their expectations and lectures.
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.
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 :)