[D] Does ML have a "bible"/reference textbook at the Intermediate/Advanced level?

A Master's student's simple question on Reddit's r/MachineLearning—"Does ML have a 'bible'?"—unlocked a definitive consensus from the professional AI community. The student, preparing for a thesis in document analysis and handwriting recognition, sought a canonical textbook akin to physics' "Jackson" for electromagnetism. The thread quickly identified Christopher M. Bishop's "Pattern Recognition and Machine Learning" (2006, Springer) as the undisputed cornerstone reference for intermediate and advanced practitioners, praised for its clarity and foundational depth in probabilistic modeling.

While Bishop's text was the clear winner, the discussion validated the student's professor-recommended list, confirming classics like Duda, Hart & Stork's "Pattern Classification" and Theodoridis & Koutroumbas's "Pattern Recognition" as essential companions. The viral moment underscores a critical tension in AI education: the breakneck pace of research versus the timeless need for strong theoretical grounding. For students and professionals alike, the community's verdict serves as a crucial guidepost, affirming that mastery of these core texts remains the most reliable path to innovating beyond today's state-of-the-art models.