| Unit name | Introduction to Machine Learning |
|---|---|
| Unit code | COMS30301 |
| Credit points | 10 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Bogacz |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | Department of Computer Science |
| Faculty | Faculty of Engineering |
This unit introduces the field of Machine Learning, and teaches how to create software that improves with experience. The syllabus of the unit includes: - Classification algorithms: Focus on Bayesian learning, and overview of other ~1001 algorithms - Applications: Spam filtering, recommendation systems, selection of adverts in search engines, XBox Kinect - General issues: Why learning from examples is possible, theoretical limitations of machine learning, comparing learning algorithms - Coursework: Programming spam filter in Java, learning Weka
After successfully completing this unit, you will be able to: Choose an appropriate learning algorithm for a given problem; Use machine learning algorithms in solving classification problems ; Understand theoretical limitations of machine learning.
20 lectures. A further 56 hours are nominally set aside for coursework, private study, etc.
50% Exam, 50% Coursework
I Witten and E Frank. Data Mining: Practical Machine Learning Tools and Techniques. 2nd Ed. Morgan Kaufman. 2005. ISBN: 0120884070 Background T Mitchell. Machine Learning. McGraw-Hill. 1997. ISBN: 0071154671 Background
