Machine Learning is a powerful new field with many important practical applications. Thanks to the information age and flood of data, it has taken many domains by storm including biology, text processing, internet data organization, computer vision, speech recognition, computer-human interfaces, robotics, and artificial intelligence. This easy-access book covers the main contemporary themes and tools in machine learning, ranging from Bayesian probabilistic models to discriminative support-vector machines. Unlike previous books, it bridges these two schools of thought together within a common framework, elegantly connecting their various theories and combining their strengths into one common big-picture.