Overview

A project-based seminar course in which students work individually or in small teams to tackle data science and machine learning problems, working with real-world datasets. The course emphasizes critical thinking about experiments and large dataset analysis and the ability to clearly communicate one's research. This course is intended to support students in developing the analytical skills necessary for quantitative independent work.  Students are not required to bring in their own project proposal and dataset for this course; however, if they do, students should consult with their home department about how this course could appropriately complement, but not replace, their independent work requirements. (Note: SML 312 is an alternative version of SML 310; the two courses are equivalent.)

FAQs

Can I use my work in SML 310 as part of my undergraduate thesis?
With permission from their thesis advisor and/or their undergraduate Department, students can incorporate work they did in SML 310 into their thesis.

Students should indicate in their thesis which parts of the work were completed as part of SML 310.
 

Can I use my work in SML 310 to fulfill the CSML Certificate’s Independent Work requirement? 
That is in principle possible if the scope of your SML 310 project is sufficiently large and the write-up is sufficiently comprehensive. However, many SML 310 students would need to expand their SML 310 project in order to fulfill the IW requirement. 
 

Can I use work that I did for an earlier project (e.g. for my JP) as my project in SML 310?
You cannot resubmit work that you already have completed elsewhere. You can build on work that you had done previously. Your write-up must clearly indicate what part of the work was done as part of SML 310, and what part of the work was done earlier.