This book investigates the fundamental principles and widely used methods of supervised machine learning. The author, Hui Jiang, assumes a foundational understanding of calculus, linear algebra, probability, and statistics, but no prior machine learning experience. The text systematically introduces core concepts and algorithms, including traditional methods like Support Vector Machines (SVMs) and Hidden Markov Models (HMMs), as well as deep learning techniques such as Convolutional Neural Networks (CNNs), attention mechanisms, transformers, and Generative Adversarial Networks (GANs). The book is structured to emphasize the overarching framework of machine learning, presenting each method clearly and concisely from first principles.

The book is designed as an accessible introduction to supervised machine learning, aiming for clarity and logical progression for beginners. Its structured approach and concise explanations of complex algorithms suggest it is intended for students or practitioners seeking a solid theoretical grounding. The inclusion of both traditional and modern deep learning techniques indicates a comprehensive scope for an introductory text. The emphasis on case studies and practical examples points to a focus on applied understanding.