About this product

Product Identifiers

PublisherOxford University Press, Incorporated

ISBN-100198714939

ISBN-139780198714934

eBay Product ID (ePID)20038906678

Product Key Features

Number of Pages384 Pages

LanguageEnglish

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

Publication Year2019

SubjectSignals & Signal Processing, Intelligence (Ai) & Semantics, Computer Science

TypeTextbook

AuthorMax A. Little

Subject AreaComputers, Technology & Engineering

FormatHardcover

Dimensions

Item Height1 in

Item Weight34.7 Oz

Item Length9.8 in

Item Width7.6 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2019-944777

Dewey Edition23

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, "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 Ã,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, "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 Âmachine 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, "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 Âmachine 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, "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 Âmachine 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"This book gives a solid mathematical foundation to, and details the key concepts and algorithmsin, this important topic." -- MathSciNet

IllustratedYes

Dewey Decimal006.310151

Table Of Content1. Mathematical Foundations2. Optimization3. Random Sampling4. Statistical Modelling and Inference5. Probabalistic Graphical Models6. Statistical Machine Learning7. Linear-Gaussian Systems and Signal Processing8. Discrete Signals: Sampling, Quantization and Coding9. Nonlinear and Non-Gaussian Signal Processing10. Nonparametric Bayesian Machine Learning and Signal Processing

SynopsisThis book 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. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications. Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic., 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.

LC Classification NumberQ325.5