Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 (Springer) - A foundational textbook offering a comprehensive mathematical treatment of machine learning, covering statistical methods, model definition, and optimization principles.
Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2019 (Cambridge University Press)DOI: 10.1017/9781108679908 - A textbook directly addressing the mathematical prerequisites for machine learning, including linear algebra for data representation and calculus for optimization techniques.
CS229 Lecture Notes, Andrew Ng, Tengyu Ma, 2023 - Lecture notes from Stanford's renowned machine learning course, detailing the mathematical foundations of various algorithms, particularly emphasizing calculus in gradient-based optimization.