Machine Learning

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This comprehensive introduction to machine learning employs probabilistic models and inference as a unifying framework. The explosion of electronic data on the Web necessitates automated data analysis methods, and machine learning addresses this by developing techniques to automatically identify patterns and predict future data. The textbook presents a self-contained overview of the field, integrating essential background topics such as probability, optimization, and linear algebra, while also covering recent advancements like conditional random fields, L1 regularization, and deep learning. Written in an informal and accessible style, it includes pseudo-code for key algorithms, along with numerous color illustrations and worked examples from diverse fields like biology, text processing, computer vision, and robotics. Instead of merely presenting a variety of heuristic methods, the book emphasizes a principled model-based approach, often utilizing graphical models for clear and concise specification. Most models discussed are implemented in the freely available MATLAB software package, PMTK (probabilistic modeling toolkit). This resource is ideal for upper-level undergraduates with a basic college math background and beginning graduate students.