MACHINE LEARNING: A PROBABILISTIC PERSPECTIVE
Machine Learning: A Probabilistic Perspective provides a comprehensive introduction to machine learning through the principles of probability, statistics, and data-driven inference. The book covers fundamental concepts including probability distributions, Bayesian learning, supervised and unsupervised learning, graphical models, optimization techniques, neural networks, and deep learning. It emphasizes the probabilistic approach to building intelligent systems capable of making predictions and decisions under uncertainty. With practical examples, real-world case studies, and modern applications in healthcare, finance, natural language processing, computer vision, and robotics, this book serves as an essential resource for undergraduate and postgraduate students, researchers, data scientists, and professionals seeking a strong theoretical foundation and practical understanding of machine learning.