Unlock the foundations of modern machine learning with ‘Learning Theory from First Principles.’ This essential textbook from the Adaptive Computation and Machine Learning Series, by Francis Bach, demystifies complex mathematical arguments, making cutting-edge learning theory accessible to graduate students and researchers. Focusing on practical algorithms and their performance, Bach builds rigorous results from the ground up, emphasizing adaptive computation and unifying statistical, optimization, and approximation theories. Discover coverage of overparameterized models, structured prediction, and hands-on experiments linking theory to real-world application. Perfect for understanding widely used machine learning architectures and their theoretical underpinnings.