While studying for the Coursera Machine Learning lecture I attended last year, my learning partner Dimitris L. recommended we should use the Bayesian Reasoning and Machine Learning book by Prof. David Barber as complementary literature. David Barber is currently a professor in Information Processing in the department of Computer Science UCL where he develops novel information processing schemes, mainly based on the application of probabilistic reasoning. As the title of the book suggests, it is all about the concepts and techniques behind Bayesian reasoning and machine learning:
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds.It is designed for final-year undergraduates and master’s students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
A really nice thing about this book is that there exists a free online version of it on the university website of Prof. Barber (03/2014):
I just thought I´d share this with you, since the book is really good for understanding some of the underlying concepts of machine learning. Have a look at the free eBook and if you like it, do as I did and get the paperback copy at your favorite bookstore.