Machine Learning 10-701/15-781, Fall 2006 Eric Xing and Tom Mitchell School of Computer Science, Carnegie-Mellon University

Basics

• Schedule and class handouts

• First class: September 12, 2006
• Class lectures: Tuesday & Thursday from 10:30-11:50am
• Location:  WEH 7500
• Recitations:  an hour slot on Tuesday or Thursday 5-7pm, NSH 1305 (check the schedule for each individual recitation)
• Textbook:
  • Textbook:  Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006 (available from the CMU bookstore, Amazon, or Springer).
  • Additional optional textbook: Machine Learning, Tom Mitchell.
  • Additional optional textbook: Pattern Classification (2nd Edition), Duda, Hart and Stork.
  • Additional readings will be made available as appropriate.

Announcements

• The final project reports are available here.
• The final exam is Friday, 12/15, 5:30-8:30pm in Wean 7500. (Final, Solutions)
• There are two review sessions for the final exam. The first one is on Tuesday, 12/12, at 5pm in NSH 1305. The second one is on Thursday, 12/14, at 10am in NSH 1305.
• The midterm exam is Thursday, 10/19, 10:30-11:50am in Wean 7500. (Midterm, Solutions, Statistics)
• Recitation schedule is posted here.

People

Instructors:

• Eric Xing, Wean Hall 4127, x8-2559, Office hours: Wednesday 16:20-17:20,
• Tom Mitchell, Wean Hall 5309, x8-2611, Office hours: by appointment

 

Class Assistant:

• Sharon Cavlovich, Wean Hall 5315, x8-5196

 

Teaching Assistant:

• Indrayana Rustandi, Wean Hall 8402, x8-3076, Office hours: Monday 16:30-17:30
• Yifen Huang, Newell-Simon Hall 4533, x8-9515, Office hours:  Thursday 13:00-14:00
• Fan Guo, Wean Hall 1315, x8-5941, Office hours: Friday 16:00-17:00
   

The class mailing list is 10701-fall06@cs. If you wish to email only the instructors, the email is 10701-fall06-instructors@cs.

Course Description

Machine learning studies the question "how can we build computer programs that automatically improve their performance through experience?"   This includes learning to perform many types of tasks based on many types of experience.  For example, it includes 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 data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you.  This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.

If you are interested in this topic, but are not a PhD student, you might consider the master's level course on Machine Learning, 15-681.

Page links

• Syllabus and course schedule
• Homework assignments
• Final project (also see Project Guidelines and Project Suggestions)
• Some previous exams

• Grading

• Homework resources and collaboration policy
• Late homework policy
• Homework regrades policy
• Exam schedule
• Note to people outside CMU.

 

Web pages for earlier versions of this course:  (include examples of midterms, homework questions, ...) 

• The Fall 2005 Machine Learning Web Page
• The Spring 2006 Machine Learning Web Page