Bayesian Reasoning and Machine Learning

Vast amounts of data present a major challenge to all those working in computer science and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning and crucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included.