Title
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series),Used
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Webenabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and selfcontained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudocode for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled modelbased approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software packagePMTK (probabilistic modeling toolkit)that is freely available online. The book is suitable for upperlevel undergraduates with an introductorylevel college math background and beginning graduate students.
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- Q: What is the main focus of 'Machine Learning: A Probabilistic Perspective'? A: The book focuses on providing a comprehensive introduction to machine learning using probabilistic models and inference as a unifying approach.
- Q: Who is the author of this textbook? A: The author of 'Machine Learning: A Probabilistic Perspective' is Kevin P. Murphy.
- Q: What topics are covered in this book? A: The book covers a range of topics including probability, optimization, linear algebra, conditional random fields, L1 regularization, and deep learning.
- Q: Is this book suitable for beginners? A: Yes, it is suitable for upper-level undergraduates with a basic college math background and beginning graduate students.
- Q: Does the book include practical examples? A: Yes, the book includes color images and worked examples from various application domains such as biology, text processing, computer vision, and robotics.
- Q: What is the format of the book? A: The book is available in hardcover format and consists of 1104 pages.
- Q: When was this book published? A: The book was published on August 24, 2012.
- Q: What kind of approach does the book take towards machine learning? A: The book emphasizes a principled model-based approach rather than just providing a cookbook of heuristic methods.
- Q: Is there any software associated with the book? A: Yes, almost all the models described in the book have been implemented in a MATLAB software package called PMTK, which is freely available online.
- Q: What edition of the book is available? A: The available edition is the illustrated edition.