Title: Machine Learning: A Probabilistic Perspective
Author: Kevin P. Murphy
Publisher: The MIT Press
Description: “Machine Learning: A Probabilistic Perspective” by Kevin P. Murphy offers a comprehensive overview of machine learning techniques from a probabilistic standpoint. In this illustrated edition, Murphy explores fundamental concepts, algorithms, and applications of machine learning in a rigorous yet accessible manner.
The book covers a wide range of topics, including supervised and unsupervised learning, graphical models, Bayesian methods, kernel machines, and deep learning. Murphy emphasizes the importance of probabilistic reasoning and uncertainty estimation in machine learning models, providing readers with a solid theoretical foundation and practical insights into real-world applications.
With its clear explanations, illustrative examples, and exercises, “Machine Learning: A Probabilistic Perspective” is suitable for students, researchers, and practitioners interested in understanding the probabilistic principles underlying modern machine learning algorithms. It serves as a valuable resource for anyone seeking to delve deeper into the field of machine learning and its probabilistic foundations.