Machine Learning

10-701/15-781, Fall 2010

Aarti Singh

Home People Lectures Recitations Homeworks Project Previous material Table of algorithms


Date and Time: Monday and Wednesday, 10:30 - 11:50 am
Location: 7500 Wean Hall

Recitation: Date and Time: Thursday, 5:00 - 6:00 pm
Location: NSH 3305 (normal location), NSH 1507 (Sep 16, 23, 30; Oct 7; Nov 11; Dec 2, 9)

Course Description:

It is hard to imagine anything more fascinating than automated systems that improve their performance through experience. Examples range from robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. Machine learning is concerned with the study and development of techniques that can automatically learn from data. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning, and related discplines and applications.

Prerequisites: Students entering the class are expected to have a pre-existing working knowledge of probability, linear algebra, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. In addition, recitation sessions will be held to revise some basic concepts.

  • Pattern Recognition and Machine Learning, Christopher Bishop.
  • Machine Learning, Tom Mitchell. (optional)
  • The Elements of Statistical Learning: Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. (optional)
  • Midterms (20%)
  • Homeworks (35%)
  • Final project (25%)
  • Final exam (20%)
Auditing: To satisfy the auditing requirement, you must either:
  • Do *two* homeworks, and get at least 75% of the points in each; or
  • Take the final, and get at least 50% of the points; or
  • Do a class project
    • Like any class project, it must address a topic related to machine learning and you must have started the project while taking this class (can't be something you did last semester). You will need to submit a project proposal with everyone else, and present a poster with everyone. You don't need to submit a milestone or final paper. You must get at least 80% on the poster presentation part of the project.
Please, send the instructors an email saying that you will be auditing the class and what you plan to do.

  • If you are on the waiting list, email the instructor and you will be allowed to enroll if there is space and you meet the pre-requisites.
  • The class mailing list is 10701-announce@cs. If you wish to email only the instructors, the email is 10701-instructors@cs .
  • If you are registered for the course, you have automatically been added to the mail group. If you are for some reason NOT receiving these announcements, you can subscribe via the 10701-announce list page .