Introduction to Machine Learning

This textbook provides a comprehensive introduction to Machine Learning techniques and algorithms, with a focus on newer approaches such as deep learning, auto-encoding, and reinforcement learning. It is written in an easy-to-understand manner with many examples and practical advice, and covers a wide range of topics including Bayesian classifiers, neural networks, and performance evaluation.

\n Format: Hardback
\n Length: 458 pages
\n Publication date: 27 September 2021
\n Publisher: Springer Nature Switzerland AG
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This comprehensive textbook provides an in-depth introduction to Machine Learning techniques and algorithms, catering to a wide range of readers. In its Third Edition, it delves into the latest and most topical approaches, such as deep learning, auto-encoding, temporal learning, and hidden Markov models. Written in a user-friendly style, the book features numerous examples, illustrations, and practical advice, making it accessible to beginners and experts alike.

The main topics covered in this textbook include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is given to performance evaluation, statistical assessment, and practical issues related to feature selection, feature construction, bias, context, multi-label domains, and the problem of imbalanced classes.

By presenting a comprehensive overview of Machine Learning concepts and applications, this textbook serves as a valuable resource for students, researchers, and practitioners in the field. Its updated content and practical insights make it an essential tool for anyone seeking to advance their knowledge and expertise in this rapidly evolving domain.

\n Weight: 864g\n
Dimension: 231 x 270 x 35 (mm)\n
ISBN-13: 9783030819347\n
Edition number: 3rd ed. 2021\n