Course Description

Contents and Outcomes:
Machine learning techniques that allow computers to form representations, make predictions, or apply controls automatically from data have become increasingly prevalent in modern technologies and are opening up new approaches in a wide range of domains. This course provides an introduction to the field of Machine Learning and covers fundamental and state-of-the-art machine learning algorithms. It will cover unsupervised, supervised, semi-supervised, as well as reinforcement learning techniques with a focus on unsupervised and supervised learning. Students completing this course will gain an understanding of the area of machine learning and the ways in which different learning algorithms operate. They will also be able to apply the covered methods to real-world problems.
Many of the techniques covered in this course are based on statistics and linear algebra and knowledge in these areas is required. Prerequisites for this course are either Data Modeling (CSE 5301), Artificial Intelligence (CSE 5361), an advanced statistics course, or consent of instructor.

Course Materials:
This textbook for this course is:
Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
Additional readings in the form of book chapters or research papers will be made available either through the engineering library or on the course web site.

E-mail and WWW page:
There is a coure web page at . All changes and supplementary course materials will be available from this site. In addition, necessary changes or important announcements will also be distributed by e-mail.

Tentative Office Hours:
Office hours for the course will be held by the instructor either in ERB 128 or in 522 ERB, TTh 11:00 - 12:00, and TTh 4:30 - 5:20. Times are subject to change and will be posted. If for some reason you can not make it to any of these office hours, please inform the instructor.

Manfred Huber