Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics - Image 1

Price loading...

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

Name: Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics
Brand: Oxford University Press

AI & Machine Learning

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

This 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.

Key Features

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

Product type: ABIS BOOK

Brand: Oxford University Press

Product Specifications

Brand: Oxford University Press
Format: hardcover
ASIN: 0198714939
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 13 August 2019
Listed Since: 10 May 2019

Barcode

No barcode data available

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

Price History & Forecast

No Price Data Available

Price Distribution

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Model-Based Deep Learning (Foundations and Trends® in Signal Processing)

Machine Learning Methods for Signal, Image and Speech Processing

Digital Signal Processing: Fundamentals, Applications, and Deep Learning

Fundamental Mathematical Concepts for Machine Learning in Science

Machine Learning Methods for Signal, Image and Speech Processing (River Publishers Series in Signal, Image and Speech Processing)

Signal Processing and Machine Learning Theory (Academic Press Library in Signal Processing)

Multimedia Signal Processing: Theory and Applications in Speech, Music and Communications

Financial Signal Processing and Machine Learning (IEEE Press)

Mathematical Foundations for Signal Processing, Communications, and Networking

Statistical Signal Processing for Neuroscience and Neurotechnology

Signal Processing and Machine Learning with Applications

Digital Signal Processing in Audio and Acoustical Engineering

Statistical Signal Processing in Engineering

Fundamentals of Signal Processing in Generalized Metric Spaces: Algorithms and Applications

Digital and Statistical Signal Processing

Starting Digital Signal Processing in Telecommunication Engineering: A Laboratory-based Course (Textbooks in Telecommunication Engineering)

Machine Learning for Future Fiber-Optic Communication Systems

Digital Signal Processing with Examples in MATLAB (Electrical Engineering & Applied Signal Processing Series)

A Mathematical Introduction to Data Science

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Machine Learning for Engineers

Fundamental Mathematical Concepts for Machine Learning in Science

Numerical Bayesian Methods Applied to Signal Processing (Statistics and Computing)

Numerical Bayesian Methods Applied to Signal Processing (Statistics and Computing)