Comp 250: Machine Learning

Fall, 2006

Carla E. Brodley

Instructor: Carla E. Brodley
Office: Halligan 213
Email: brodley [at] cs [dot] tufts [dot] edu
Office Hours: By appointment

TA: D. Sculley
Email: dsculley [at] cs [dot] tufts [dot] edu
Office Hours: Wednesday, 2:00pm and by appointment.

Course Syllabus

The course syllabus is available here.

Course Description

Machine learning is concerned with computer programs that automatically improve their performance through experience. Knowledge discovery in databases is concerned with ex- tracting useful patterns or deviations from data using data mining methods. This course introduces students to the primary approaches to machine learning and data mining from a variety of fields, including inductive inference of decision trees, neural network learning, statistical learning methods, clustering, and discovery. Evaluation will be based on the student's course projects and synopses of assigned reading.

The schedule of student class project presentations is available here.

For more information about this course, including grading policies and important dates, see the course introduction handout.

Mailing List

Students in the course should be signed up for the course mailing list. If you have not yet recieved a confirmation email welcoming you to this list, please email the TA.

Critiques

A major part of the course is reading papers and writing critiques. The grading rubric for critiques will be used to evaluate the critiques. Be sure to staple a copy of this rubric to each submitted critique, along and complete the self-assessment.

Poster Session

The class poster presentation session will be held on Wednesday, October 4th, from 5-7:00pm. The list of papers to be presented at the session is available here. Guidelines for a strong poster and poster presentation are listed here.

Additional Resources

A list of machine learning resources is available here. Demo code for exploring different kernels with XOR is available here.