This book is based on material taught in a machine learning course in the
School of Computing Science at the University of Glasgow, UK. The course,
presented to nal year undergraduates and postgraduates, is made up of 20
hour-long lectures and 10 hour-long laboratory sessions. In such a short teach-
ing period, it is impossible to cover more than a small fraction of the material
that now comes under the banner of machine learning. Our intention when
teaching this course therefore, is to present the core mathematical and statistical techniques required to understand some of the most popular machine
learning algorithms and then present a few of these algorithms that span
the main problem areas within machine learning: classication, clustering and
projection. At the end of the course, the students should have the knowledge
and condence to be able to explore the machine learning literature to nd
methods that are more appropriate to them.