Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David

Understanding Machine Learning: From Theory to Algorithms is a foundational academic textbook published by Cambridge University Press that connects machine learning theory and practical algorithms. Written by renowned researchers Shai Shalev-Shwartz and Shai Ben-David, this thorough resource provides rigorous mathematical foundations for understanding how machine learning systems work. The book covers essential concepts including learning theory, computational complexity, regularization, and various algorithmic approaches. It's designed for students, researchers, and practitioners who want to develop a deep theoretical understanding of machine learning beyond surface-level applications. The text emphasizes the 'why' behind algorithms, making it very useful for anyone seeking to build solid ML systems or contribute to the field's advancement.