CIS 472/572, Machine Learning

CIS 472/572, Winter 2014

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 4:00pm - 5:20pm in 202 Cascade Hall.
Instructor: Dejing Dou, 303 Deschutes, phone 6-4572, email dou@cs.uoregon.edu.
GTF: Matthew (Hang) Xu, 232 Deschutes, phone 6-4469, email hang@cs.uoregon.edu.
Office hours:
- Dejing Thursdays 2:00-3:30 or by appointment (I like to talk with students).
- Matthew Tuesdays 3:30-5:00
Directory: The (unix) directory for the class is /cs/classes/www/14W/cis472.
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 28 and Part 2 (final report) on Friday Mar 21. 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 (February 12) 20%

Final (March 18) 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 6	1	intro, learning system design;
Jan 13	2,3	concept learning, decision tree learning
Jan 20	3	decision tree learning, bagging and boosting	classic paper reading: Bagging, Boosting, and C4.5
Jan 27	4	artificial neural network
Feb 3	6	Bayesian learning, deep learning	classic paper reading: deep belief nets
Feb 10		Midterm review on Monday	Midterm Wednesday, Feb 12; paper reading: SVM, SVM application
Feb 17	6,additional	EM, SVM, multiple kernel learning	paper reading: multiple kernel learning
Feb 24	10	learning rules	Course Project Part 1: Friday, February 28
March 3	10, 13	inverted resolution, reinforcement learning, final review	classic paper reading: FOIL
Mar 10		final and assignment review, project implementation
Mar 17		finals week;	Final exam Tuesday, 3:15pm - 5:15pm, Mar 18; Course Project Part 2: Fri, Mar 21

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:

Homework Assignments	40%
Midterm (February 12)	20%
Final (March 18)	20%
Course Project	20%