Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics

This book provides a comprehensive introduction to the fundamental mathematics and algorithms of machine learning and signal processing, two critical technologies in the modern information economy. It builds concepts step-by-step, enabling practical implementation in software applications. DSP is essential for developing technologies like mobile phones, while statistical machine learning underpins exciting applications like speech recognition and autonomous car navigation. Both subjects make use of applied mathematics, including probability and statistics, algebra, calculus, graphs, and networks, and there are many overlaps between them that can be exploited to create new DSP tools.

Format: Paperback / softback
Length: 384 pages
Publication date: 28 February 2024
Publisher: Oxford University Press

This comprehensive text delves into the intricate fundamentals of mathematics and algorithms, providing a deep understanding of machine learning and signal processing, two of the most influential and rapidly advancing technologies in the modern information economy. With a gradual and systematic approach, it builds upon concepts step by step, enabling readers to apply these ideas and methodologies in practical software applications.

Digital signal processing (DSP) stands as a cornerstone engineering discipline, indispensable to the development of numerous cutting-edge technologies. Without DSP, the existence of devices like mobile phones, televisions, CD and MP3 players, WiFi, radar, and countless other innovations would be impossible. While DSP has been a foundational element for decades, statistical machine learning has emerged as a relatively recent field, revolutionizing the realm of exciting technologies such as automatic car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation.

Statistical machine learning harnesses the analogy between intelligent information processing in biological brains and sophisticated statistical modeling and inference. By leveraging this analogy, it develops algorithms that can learn from data and make predictions or decisions based on patterns. This field has experienced rapid advancements and expanded its scope and applicability, making it a key player in the knowledge economy.

DSP and statistical machine learning share numerous commonalities, including the use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs, and networks. These mathematical foundations provide a solid basis for understanding and developing advanced DSP tools and techniques.

Due to the intimate formal links between DSP and statistical machine learning, there exist numerous overlaps between the two subjects. These overlaps can be exploited to create new DSP tools that possess surprising utility and are highly suited to the contemporary world characterized by pervasive digital sensors and affordable high-powered computing hardware.

This book serves as a valuable resource for individuals seeking to establish a strong mathematical foundation in machine learning and signal processing. It provides detailed explanations of key concepts, algorithms, and applications, making it accessible to students, researchers, and professionals in these fields. By exploring the interplay between DSP and statistical machine learning, readers will gain insights into the latest developments and advancements, enabling them to contribute to the ongoing evolution of these technologies and shape the future of information processing.

Weight: 820g
Dimension: 205 x 441 x 31 (mm)
ISBN-13: 9780198896555