Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Summary: “Probabilistic Graphical Models: Principles and Techniques” provides a comprehensive overview of probabilistic graphical models (PGMs) and their applications in machine learning and artificial intelligence. Covering topics such as Bayesian networks, Markov networks, inference algorithms, and learning methods, this book offers insights into the principles and techniques underlying PGMs. With a focus on both theory and practical applications, the book provides readers with the knowledge and skills needed to design and deploy PGMs for various tasks, including classification, regression, and clustering. Whether you’re a student of machine learning, a researcher, or a practitioner in the field of artificial intelligence, this book serves as an invaluable resource for understanding and applying probabilistic graphical models.
Author Information: The authors of “Probabilistic Graphical Models: Principles and Techniques,” Daphne Koller and Nir Friedman, are renowned researchers in the field of artificial intelligence and machine learning, with expertise in probabilistic modeling and graphical models.
Publisher: The MIT Press
Publication Year: 2009
ISBN-10: 0262013193
ISBN-13: 978-0262013192