Course Description

Machine learning studies the question "how can we build computerprograms that automatically improve their performance throughexperience?" This includes learning to perform many types of tasksbased on many types of experience. For example, it includes robotslearning to better navigate based on experience gained by roamingtheir environments, medical decision aids that learn to predict whichtherapies work best for which diseases based on data mining ofhistorical health records, and speech recognition systems that lean tobetter understand your speech based on experience listening to you.This course is designed to give PhD students a thorough grounding inthe methods, theory, mathematics and algorithms needed to do researchand applications in machine learning. The topics of the course drawfrom from machine learning, from classical statistics, from datamining, from Bayesian statistics and from information theory.Students entering the class with a pre-existing working knowledge ofprobability, statistics and algorithms will be at an advantage, butthe class has been designed so that anyone with a strong numeratebackground can catch up and fully participate. If you are interestedin this topic, but are not a PhD student, or are a PhD student notspecializing in machine learning, you might consider the master'slevel course on Machine Learning, 10-601."

Textbook

Grading

The requirements of this course consist of participating in lectures, midterm and final exams, 5 problem sets and a project. This is a PhD level class, and the most important thing for us is that by the end of this class students understand the basic methodologies in machine learning, and be able to use them to solve real problems of modest complexity. The grading breakdown is the following:

  • Homework (5 assignments, 30%)
  • Midterm (25%)
  • Final exam (25%)
  • Final project (20%)

Exams

  • The midterm and final exams will be open book and open notes.  Computers will not be allowed.
  • Midterm exam date: March 3rd
  • Final exam date: TBA
  • Rescheduling of exams:  It is impossible for us to accommodate individual requests to reschedule the exams. It is your responsibility to assure that you are in town and available for the final exam.

Homework resources and collaboration policy

Homeworks and exams may contain material that has been covered by papers and webpages. Since this is a graduate class, we expect students to want to learn and not google for answers.

Homeworks will be done individually: each student must hand in their own answers. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. You also must indicate on each homework with whom you collaborated.

The final project may be completed by small teams.

Late homework policy

  • You will be allowed 2 total late days without penalty for the entire semester.  You may be late by 1 day on two different homeworks or late by 2 days on one homework. Once those days are used, you will be penalized according to the following policy:
    • Homework is worth full credit at the beginning of class on the due date.
    • It is worth half credit for the next 48 hours.
    • It is worth zero credit after that.
  • You must turn in at least n-1 of the n homeworks, even if for zero credit, in order to pass the course.
  • Turn in all late homework assignments to Michelle Martin.

Homework regrades policy

If you feel that we have made an error in grading your homework, please turn in your homework with a written explanation to  Michelle Martin and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.

 

Note to people outside CMU

Please feel free to reuse any of these course materials that you find of use in your own courses.  We ask that you retain any copyright notices, and include written notice indicating the source of any materials you use.

© 2010 Eric Xing @ School of Computer Science, Carnegie Mellon University
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