Publisher’s description: Machine Learning is a powerful new field with many important practical applications. It has recently matured from a black art into a principled science with a strong mathematical and statistical foundation. Thanks to the information age and flood of data, it has also taken many domains by storm including biology, text processing, internet data organization, computer vision, speech recognition, computer-human interfaces, robotics and artificial intelligence. Engineers and companies are looking to these technologies to gain a competitive edge. From the smallest startups that are using support vector machines for web page classification, to biotech firms that are doing drug discovery and large corporations that are building learning into database systems, the tools of this field are proliferating.
Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning.
Machine Learning: Discriminative and Generative is designed for an audience composed of researchers and practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.