Created:
Wed 04 Sep 2013
Last modified:
Instructor
Teaching Assistants
Course
- Meets:
Mon/Wed 2:50--4:30pm
- Room:
102 WVG
Prerequisite
-
CS5800 (Algorithms). Machine Learning is largely self-contained.
Texts
- Machine Learning: A Probabilistic Perspective
by Kevin Murphy. Recommended, but not required.
- Pattern Classification (second edition)
by Duda, Hart, and Stork. Recommended, but not required.
Homeworks and Projects
- There will be approximately six written homeworks and/or coding projects
assigned throughout the term. The exact schedule of these assignments will be
posted on the syllabus page.
Project
- There will be a final project which will involve coding and a written report.
Grading
- Homeworks: 3/4
- Final Project: 1/4
Academic Honesty
- All work submitted for credit must be your own.
- You may discuss the assignments with your classmates, the TAs, and
Professor Aslam. You must acknowledge the people with whom you
discuss your work, and you must write your own code and written
solutions. Any sources used (apart from the text) must also be
acknowledged.
- Please ask if you have any questions about academic honesty as it
applies to CS6140.
Switch to:
jaa@ccs.neu.edu