Machine Learning Methods

Li, Hang; Lin, Lu; Zeng, Huanqiang

In an era where artificial intelligence (AI) is transforming every industry, mastering a core set of machine learning methods is essential for both understanding and applying modern AI technologies. Machine Learning Method (Second Edition) stands out as a rigorously structured, method-oriented guide that systematically presents the most foundational and widely used techniques across four key branches: supervised learning, unsupervised learning, deep learning, and reinforcement learning.

This open access book is featured with its clear organization around algorithmic methods—such as GBDT, EM algorithm, Transformer, diffusion models, and PPO—that have remained central to machine learning despite rapid advancements in the field. Through concise mathematical formulations, intuitive explanations, and practical examples, the book offers deep insights into over 80 essential techniques. Each volume provides a focused overview, followed by chapters that explicate one or two key methods, making the content accessible for comprehensive study or targeted reference.

Designed for advanced undergraduate and graduate students, educators, and AI professionals, this book serves both as a textbook and a long-term reference. It assumes foundational knowledge in calculus, linear algebra, probability, and computer science, and rewards readers with a structured understanding of machine learning that is both theoretical and application-ready. Whether you're curious about why Transformers revolutionized NLP, or how PPO optimizes decision-making in reinforcement learning, this book will not only inform but also inspire further exploration.

Details

Published by: Springer

Publication Date: 2026-10-21

Format: Hardcover

ISBN-13: 9789819223008

DOI:

Dimensions: 235cm x155cm

Pages: