Computer Science 150RML
Department of Computer Science
Tufts University
Spring 2012

Course Web page (this page)

  • (1/17) pages under construction

Instructors: Carla Brodley: and Roni Khardon:

Prerequisites: COMP 135 or COMP 136
Syllabus: This course will be built around several concrete real-world problems suitable for analysis using machine learning. Students will be grouped into teams and will investigate/invent the machine learning algorithms needed for the chosen application (example applications include predictive medicine, collaboration prediction, text classification, geography and collaborative filtering). Background material on the applications and relevant machine learning research literature will be provided in early lectures. The required course work will be 1) reading current literature related to the chosen research topic; 2) writing code with new ideas, writing code to compare to ideas in the literature, running experiments, understanding how to evaluate performance for the particular application, and most importantly being creative; 3) writing a paper on your research with your team and the instructors; 4) presenting your research to the group; and 5) one or two weekly meetings with your group and the instructors. By taking this course a student will obtain a more in-depth understanding of core machine learning ideas, an appreciation for the issues that arise when applying machine learning in practice, and experience in how to do research in machine learning.

Detailed schedule, assignments etc (restricted access)