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

Little, Max A.

買這商品的人也買了...

相關主題

商品描述

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.

商品描述(中文翻譯)

本書詳細描述了機器學習(人工智慧的一個例子)和信號處理這兩個在現代信息經濟中最重要且令人興奮的技術的基本數學和算法。本書以漸進的方式逐步建立概念,使得這些想法和算法可以應用於實際的軟體應用中。

數字信號處理(DSP)是現代世界中的「基礎」工程主題之一,沒有它,諸如手機、電視、CD和MP3播放器、WiFi和雷達等技術將不可能存在。相對較新的統計機器學習是令人興奮的技術的理論基礎,例如自動車牌識別技術、語音識別、股市預測、裝配線上的缺陷檢測、機器導航和自動駕駛汽車導航。統計機器學習利用生物大腦中智能信息處理和複雜統計建模和推斷之間的類比。

DSP和統計機器學習對知識經濟至關重要,兩者都經歷了快速變化和範圍和應用性的根本改進。兩者都利用應用數學的關鍵主題,如概率和統計、代數、微積分、圖形和網絡。兩個主題之間存在緊密的形式聯繫,因此兩個主題之間存在許多重疊,可以利用這些重疊來產生新的DSP工具,非常適合於當今普遍存在的數字傳感器和高性能但便宜的計算硬體的世界。本書為這一重要主題提供了堅實的數學基礎,並詳細介紹了關鍵概念和算法。

作者簡介


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.

作者簡介(中文翻譯)

Max A. Little,英國阿斯頓大學數學教授,是信號處理和機器學習領域的世界領先專家。他在數字健康機器學習方面的研究具有很大的影響力,並成為量化神經系統疾病(如帕金森病)基礎和應用研究的基礎。他在該領域的科學文獻上發表了60多篇文章,擁有兩項專利和一本教科書。他是政府和國際領先企業在健康機器學習等領域的顧問。