CS 480/580L Introduction to Machine Learning Spring 2007
Goals:
This course provides a broad introduction to machine learning and its applications. It will introduce students the basic ideas and intuition behind different machine learning techniques as well as a more formal understanding of how and why they work. The course will also discuss recent applications of machine learning, such as to data mining, bioinformatics, and information retrieval.
This course is designed for CS graduate students and senior undergraduate students.
Class Schedule: T TH 6:00 PM  7:25 PM  Classroom: FA 245 
Instructor: Dr.
Lei Yu 
TA: Li Chen 
Telephone: (607) 7776250  
Email: lyu AT cs DOT binghamton DOT edu  Email: lchen8 AT binghamton DOT edu 
Office Location: N 26, Engineering Building  Office Location: N 1, Engineering Building 
Office Hours: T TH 12:30PM  1:30PM or by appointment  Office Hours: T 2:00PM3:30PM, W 1:00PM2:30PM 
Prerequisite:
Topics:
Major topics include:
Textbook (recommended):
Introduction to Machine Learning, Ethem Alpaydin, The MIT Press, 2004.
Machine Learning, Tom Mitchell, McGraw Hill, 1997.
Homework:
There will be 4 written assignments during the semester.
Programming Assignments:
There will be two programming assignments involving implementation of Java code for experiments with classification and clustering algorithms in Weka (an opensource software collection of machine learning algorithms).
Projects (required only for graduate students):
Each graduate student is required to conduct a small scale term project. The topic and format of each project varies according to the interest and background of each student. Acceptable formats include (i) literature survey on a particular topic, (ii) implementation and experimentation with a machine learning algorithm introduced by a recent technical paper, (iii) comparative evaluation of a group of related machine learning algorithms, (iv) application of machine learning techniques on research or realworld problems, and (v) design and empirical study of a novel machine learning algorithm. Each graduate student is required to present in class a project proposal in the beginning of the semester, and submit and present a final project report at the end of the semester.
Grading:
For graduate students, final grades will be based on class participation (10%), homework (4 assignments, 20%), programming assignments (2 assignments, 30%), project (40%).
For undergraduate students, final grades will be based on class participation (10%), homework (4 assignments, 40%), programming assignments (2 assignments, 50%).
Collaboration Policy:
Discussion of general concepts and questions concerning the homework and programming assignments among students is encouraged. However, each of you is expected to work on the homework solutions and programs on your own. Sharing of any part of solutions or any segment of code is prohibited. If you are unclear about the policy, please consult with the instructor before you act. Suspected cases of academic misconduct will be pursued fully in accordance to the Student Academic Honesty Code of Binghamton University.
Late Policy:
Each assignment is due at the beginning of class on the due date. Any assignment received within the next 24 hours will be penalized by 20% of the full credit; any assignment received within the time between 24 hours and 48 hours pass the deadline is penalized by 50% of the full credit; No assignment will be accepted after 48 hours pass the deadline. Rare exceptions of this policy may be made at the discretion of the instructor under demonstrably circumstances.

Last updated on 02/22/2007 