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Cover

Machine Learning for Signal Processing

Data Science, Algorithms, and Computational Statistics

Max A. Little

August 2019

ISBN: 9780198714934

384 pages
Hardback
246x189mm

Price: £55.00

Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.

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Description

Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.

  • Self contained
  • Graduated, self-referencing, step-by-step layout allows for easy comprehension
  • Contains explicit algorithms that can be directly implemented in software
  • Utilises basic university-level mathematics, making it accessible to students across mathematics, engineering, and physics

About the Author(s)

Max A. Little, Professor of Mathematics, Aston University, Birmingham

Max A. Little is Professor of Mathematics at Aston University, UK, and a world-leading expert in signal processing and machine learning. His research in machine learning for digital health is highly influential and is the basis of advances in basic and applied research into quantifying neurological disorders such as Parkinson disease. He has published over 60 articles in the scientific literature on the topic, two patents, and a textbook. He is an advisor to government and leading international corporations in topics such as machine learning for health.

Table of Contents

    1: Mathematical Foundations
    2: Optimization
    3: Random Sampling
    4: Statistical Modelling and Inference
    5: Probabalistic Graphical Models
    6: Statistical Machine Learning
    7: Linear-Gaussian Systems and Signal Processing
    8: Discrete Signals: Sampling, Quantization and Coding
    9: Nonlinear and Non-Gaussian Signal Processing
    10: Nonparametric Bayesian Machine Learning and Signal Processing

Reviews

"This book provides an excellent pathway for gaining first-class expertise in machine learning. It provides both the technical background that explains why certain approaches, but not others, are best practice in real world problems, and a framework for how to think about and approach new problems. I highly recommend it for people with a signal processing background who are seeking to become an expert in machine learning." - Alex 'Sandy' Pentland, Toshiba Professor of Media Arts and Sciences, Massachusetts Institute of Technology,

"Over the past decade in signal processing, machine learning has gone from a disparate research field known only to people working on topics such as speech and image processing, to permeating all aspects of it. With this book, Prof. Little has taken an important step in unifying machine learning and signal processing. As a whole, this book covers many topics, new and old, that are important in their own right and equips the reader with a broader perspective than traditional signal processing textbooks. In particular, I would highlight the combination of statistical modeling, convex optimization, and graphs as particularly potent. Machine learning and signal processing are no longer separate, and there is no doubt in my mind that this is the way to teach signal processing in the future." - Mads Christensen, Full Professor in Audio Processing, Aalborg University, Denmark,