Fundamentals of Pattern Recognition and Machine Learning

The book Fundamentals of Pattern Recognition and Machine Learning is a comprehensive textbook that covers the theory and practice of pattern recognition and machine learning. It is suitable for a one or two-semester introductory course at the graduate or advanced undergraduate level. The book combines theory and practice and is designed for the classroom and self-study. It covers classical theorems in the area and includes examples with datasets from applications in bioinformatics and materials informatics.

Format: Paperback / softback
Length: 357 pages
Publication date: 11 September 2021
Publisher: Springer Nature Switzerland AG

The Fundamentals of Pattern Recognition and Machine Learning is a comprehensive textbook designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. Authored by Dr. Jianzhong Zhang, a professor at Texas A&M University, the book combines theory and practice, making it suitable for both classroom instruction and self-study. With over 13 years of teaching experience in this field, Dr. Zhang has developed this text based on his lecture notes and assignments, ensuring it covers the essential tools and topics commonly used in pattern recognition and machine learning.

The book aims to be concise yet thorough, providing a comprehensive coverage of the tools commonly employed in pattern recognition and machine learning. These tools include classification, dimensionality reduction, regression, clustering, and recent popular topics such as Gaussian process regression and convolutional neural networks. Additionally, the book includes an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification.

Mathematically rigorous, the book covers the classical theorems in the area while also striving to strike a balance between theory and practice. To illustrate the theory, examples with datasets from applications in bioinformatics and materials informatics are used throughout the text. These datasets are available from the book website, along with python scripts used to generate all the plots in the text.

The Fundamentals of Pattern Recognition and Machine Learning is an invaluable resource for students, researchers, and practitioners in the field of Pattern Recognition and Machine Learning. Its comprehensive coverage, rigorous mathematical approach, and practical examples make it an essential tool for anyone seeking to advance their knowledge and skills in this rapidly evolving field.

Weight: 714g
Dimension: 254 x 178 (mm)
ISBN-13: 9783030276584
Edition number: 1st ed. 2020