Unit information: Introduction to Machine Learning in 2009/10

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

Description including Unit Aims

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 two algorithms: decision trees, Bayesian learning, and overview of other ~1001 algorithms - Other learning tasks: Overview of datamining, regression and unsupervised learning - General issues: Why learning from examples is possible, theoretical limitations of machine learning, comparing learning algorithms The main coursework of the unit involves creating a spam filter. The second smaller coursework familiarizes with datamining software called Weka.

Assessment Information

50% Exam, 50% Coursework

Reading and References

• Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997. ISBN 0-07-042807-7.

• I.H. Witten & E. Frank. Data Minining: Practical Machine Learning Tools and Techniques, 2nd Ed. Morgan Kaufmann, 2005