A First Course in Machine Learning

"Machine Learning is rapidly becoming one of the most important areas of general practice, research and development activity within Computing Sci- ence. This is re ected in the scale of the academic research area devoted to the subject and the active recruitment of Machine Learning specialists by major international banks and nancial institutions as well as companies such as Microsoft, Google, Yahoo and Amazon. This growth can be partly explained by the increase in the quantity and diversity of measurements we are able to make of the world. A particularly fascinating example arises from the wave of new biological measurement technologies that have preceded the sequencing of the first genomes. It is now possible to measure the detailed molecular state of an organism in manners that would have been hard to imagine only a short time ago. Such measurements go far beyond our understanding of these organisms and Machine Learning techniques have been heavily involved in the distillation of useful structure fromthem. This book is based on material taught on a Machine Learning course in the School of Computing Science at the University of Glasgow, UK. The course, presented to nal year undergraduates and taught postgraduates, is made up of 20 hour-long lectures and 10 hour-long laboratory sessions. In such a short teaching period, it is impossible to cover more than a small fraction of the material that now comes under the banner of Machine Learning. Our inten- tion 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: classi cation, clus- tering"--

Simon Rogers is a lecturer in the School of Computing Science at the University of Glasgow, where he teaches a masters-level machine learning course on which this book is based. Dr. Rogers is an active researcher in machine learning, particularly applied to problems in computational biology. His research interests include the analysis of metabolomic data and the application of probabilistic machine learning techniques in the field of human-computer interaction.

Mark Girolami is a chair of statistics and an honorary professor of computer science at University College London, where he is also the director of the Centre for Computational Statistics and Machine Learning. An EPSRC Advanced Research Fellow, an IET Fellow, and a Fellow of the Royal Society of Edinburgh, Dr. Girolami has made major contributions to the field, including his generalisation of independent component analysis, his work on inference in systems biology, and his innovations in statistical methodology.