CSE 591: Machine Learning and Applications

Lecture Hours (Fall 2005): M, W 11:40 am - 12:55 pm (BYAC 270)

Instructor:	Dr. Jieping Ye
Email:	Jieping.Ye@asu.edu
Office hours:	M, W 10:00—11:00 am
Telephone:	480-727-7451

Overview

Machine Learning is the study of computer systems that automatically improve their performance through experience. This is an advanced course that will focus on the recent literature on the application of machine learning to problems from a range of different areas, including bioinformatics, computer vision, and informational retrieval. After introductory lectures, subsequent classes will focus on research papers, usually one to two papers per class. Main topics include

Clustering
Bi-clustering
Classification
Semi-supervised learning
Feature reduction
Kernel learning
Manifold learning

This course should leave you with a good knowledge of state-of-the-art machine learning methods, and an understanding of which tools are appropriate for which tasks in real-world applications.

Course Materials:

· Lecture Notes

· Homework

· Presentation

· Reference

TA: Cindy Xu

Office: 371 BA

E-mail: Xinyu.xu@asu.edu

Office Hours: Th 1—2pm

Prerequisites: Basics of linear algebra.
Course Textbook: No textbook is required for this course.

Grading

Homework (2): 30%. There will be two homework assignments assigned. (HW 2 is due on Nov 16)
Project: 50%. Two to three students form a group to carry out a small research project. It can be an implementation and a comparative study of existing methods, a review of a specific topic, or the development of a new idea. Project proposal is due during the fourth week of classes. The immediate project report is due during the twelfth week of classes (Nov 9). The final project report is due during the last week of classes (Dec 5).
Presentation: 20%. Each student will present one paper. A list of papers for presentation will be provided during the first few weeks of classes.

Syllabus PDF

Academic Integrity