CIS 410/510, Machine Learning

CIS410/510, Winter 2009

Machine Learning

[ Assignments | CIS | News]

Course Description: Machine learning is the field which concerns how to construct computer programs that automatically improve with experience. It has successful applications, such as to data mining, information retrieval, robotics and biomedical informatics. The intent of this course is to present a broad introduction to machine learning and its established algorithms, including discussions of each of the major approaches currently being investigated. The topics include neural networks, decision trees, Bayesian learning, support vector machines (kernel methods), inductive logic programming and reinforcement learning. One secondary goal is to discuss the connections between machine learning and other fields, such as AI, statistics, informatics and Web.
Time and Place: Mondays and Wednesdays at 2:00pm - 3:20pm in 200 Deschutes Hall.
Instructor: Dejing Dou, 303 Deschutes, phone 346-4572, email dou@cs.uoregon.edu.
GTF: TBA
Office hours:
- Dejing Fridays 3:30-5:00 or by appointment (I like to talk with students).
Directory: The (unix) directory for the class is /cs/classes/www/09W/cis410ml.
News: Keep an eye on the course news for miscellaneous announcements.
Evaluation:
- The homework will be a combination of written and programming work.
- The Course Project will be due in parts: Part 1 (proposal) is due Friday Feb 27 and Part 2 (final report) on Friday Mar 20. We assume that you will work on it individually. If you wish to work in a group, please check with the instructor.
The approximate weighting of the work done will be

Homework Assignments 40%

Midterm (Feb 11) 20%

Final (March 17) 20%

Course Project 20%
Text:
- (Required) Tom Mitchell, Machine Learning, McGraw Hill, 1997.(text website)

Approximate Syllabus and Lecture Slides

Week of	Chapter	Description and Slides	Events and Readings

Jan 5	1	intro, learning system design;
Jan 12	2	concept learning, general-to-specific ordering
Jan 19	3	decision tree learning	classic paper reading: C4.5
Jan 26	3,4	bagging and boosting, artificial neural network;
Feb 2	5,6	statistics intro, Bayesian learning;
Feb 9		Midterm review on Monday	Midterm Wednesday, Feb 11 paper reading: SVM, SVM application
Feb 16	additional	SVM	paper reading: multiple kernel learning
Feb 23	10	multiple kernel learning, learning rules	Course Project Part 1: Fri, Feb 27
Mar 2	10, 13	ILP, reinforcement learning	classic paper reading:,FOIL
Mar 9		course project implementation, no lectures
Mar 16		finals week;	Final exam Tue 3:15pm-5:15pm, Mar 17; Course Project Part 2: Fri, Mar 20

Late policy: All assignments are due by 11:59pm of the stated due date, though it is much safer to turn them in at class time. Late papers are subject to a 10% per day penalty. Late assignments will not be accepted once the graded papers are returned or once sample solutions are handed out. It is your responsibility to turn in the assignments on time. Make sure that electronically submitted assignments are actually delivered.
Course resources:
- Text website.
- Machine Learning Information Site

Homework Assignments	40%
Midterm (Feb 11)	20%
Final (March 17)	20%
Course Project	20%