Machine Learning

Machine Learning is a core area of AI and this textbook offers a comprehensive and unbiased introduction to its fundamentals and advanced topics, including decision trees, neural networks, and reinforcement learning. It is suitable for undergraduate and postgraduate students and researchers in computer science and related fields.

Format: Paperback / softback
Length: 459 pages
Publication date: 22 August 2022
Publisher: Springer Verlag, Singapore

Machine Learning, a fundamental and pivotal component of artificial intelligence (AI), is propelling the AI field to unprecedented heights and solidifying its status as one of the most captivating domains within computer science research. This comprehensive textbook provides a thorough and impartial introduction to nearly all facets of machine learning, spanning from the foundational principles to advanced topics. Comprising 16 chapters organized into three parts, Part 1 (Chapters 1-3) delves into the core concepts of machine learning, encompassing terminology, fundamental principles, evaluation, and linear models. Part 2 (Chapters 4-10) showcases renowned and widely employed machine learning techniques, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction, and metric learning. Part 3 (Chapters 11-16) explores some cutting-edge topics, including feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter is accompanied by exercises and additional reading materials to facilitate further exploration of specific areas of interest.

This textbook serves as an invaluable resource for undergraduate and postgraduate students pursuing computer science, computer engineering, electrical engineering, data science, and related majors. It is also a valuable reference tool for researchers and practitioners engaged in the field of machine learning. By comprehensively covering the spectrum of machine learning, from its foundational concepts to its advanced applications, this textbook equips readers with the knowledge and skills necessary to excel in this rapidly evolving field. Whether you are a novice seeking to gain a foundational understanding of machine learning or an experienced professional seeking to advance your expertise, this textbook is an essential companion on your journey.

Weight: 794g
Dimension: 240 x 168 (mm)
ISBN-13: 9789811519697
Edition number: 1st ed. 2021