Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, 2012 (MIT Press) - Essential for understanding the probabilistic foundations of machine learning, where Bayes' Theorem is applied across numerous models and algorithms.
Bayesian Data Analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013 (CRC Press) - A standard reference for Bayesian statistical methods, detailing the theory and practical applications of Bayes' Theorem for advanced data analysis.
Probabilistic Systems Analysis and Applied Probability, John Tsitsiklis, 2013 (MIT OpenCourseWare) - Provides high-quality lecture materials and problem sets from an MIT course, offering a detailed and practical approach to understanding Bayes' Theorem.