CS6140 Machine Learning

Basic Information

Created: Mon 2 May 2005
Last modified: 

Instructor

• Name: Virgil Pavlu

• Office: 338 WVH

• E-mail: vip@ccs.neu.edu

Course

• Lecture: Mondays, 6-9 pm, 2 lectures at a time. Room: 039 Snell Library.

• Office hours:
  

Text

• REQUIRED

  Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, and David G. Stork

  

• OPTIONAL

  Pattern Recognition and Machine Learning by C. Bishop

  Introduction in Machine Learning by Ethem Alpaydin

  Machine Learning by Tom Mitchell 

Prerequisites

• Linear algebra tutorial

  http://www.stanford.edu/class/cs229/section/cs229-linalg.pdf

• Probabilities tutorial

  http://www.stanford.edu/class/cs229/section/cs229-prob.pdf

• Convexity tutorial

  http://www.stanford.edu/class/cs229/section/cs229-cvxopt.pdf

  http://www.stanford.edu/class/cs229/section/cs229-cvxopt2.pdf

• Programming: you should be familiar with basic programming in an imperative language like  Java, Perl or MATLAB. While the programing language is your choice, we strongly encourage you to use MATLAB, or  any language with good math support.
  

Homework

• There will be four or five written assignments, including programming ; please see the syllabus for an exact schedule. Some of the problems will be difficult, and it will often be helpful to discuss them with others. Feel free to form study groups. However, the idea is for everyone to understand the problems and experience working through the solutions, so you may not simply "give" a solution to another classmate. In particular, each student must write up his or her own homework solutions and must not read or copy the solutions of others. If you work with others on a problem, you must note with whom you discussed the problem at the beginning of your solution write-up.

• Late homework policy: Homework is due at the beginning of class on the announced due date. You will be granted one homework extension of 1 week, to be used at your discretion, no questions asked. This policy does not apply to projects. After the first late assignment, unexcused late assignments will be penalized 20% per calendar day late. I normally will not accept assignments after the date on which the following assignment is due or after the solutions have been handed out, whichever comes first. If you will have a valid reason for turning in an assignment late, please see me in advance to obtain full-credit.

Projects

• There will be one major project. You can choose out  of several possibilities.

Exam

Grading

• Homeworks 50%
• Project   50%
• 
  

Academic Honesty

• All work submitted for credit must be your own.

• You may discuss the homework problems or projects with your classmates, the teaching assistant(s), and instructor. You must acknowledge the people with whom you discussed your work, and you must write up your own solutions. Any written sources used (apart from the text) must also be acknowledged; however, you may not consult any solutions from previous years' assignments whether they are student or faculty generated.
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