Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Probabilistic Machine Learning: An Introduction presents the foundations of a field that brings together probability, statistics, and machine learning. It explores how uncertainty can be represented and how models can learn from data, providing an accessible starting point for readers interested in computational and statistical approaches.
About This Book
This book introduces probabilistic machine learning, a field that combines probability, statistics, and machine learning.
It focuses on methods for representing uncertainty and drawing conclusions from data.
The book is part of the Adaptive Computation and Machine Learning series and is intended as an introductory treatment of the subject.
Its topic makes it relevant to readers studying modern computational methods, statistical modeling, and data-driven learning.
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