Machine Learning for Signal Processing: Data Science,Algorithms,and Computational Statistics
by: Max A. LittlePrint Length 页数: 384 pages
Publisher finelybook 出版社: Oxford University Press (October 13,2019)
Language 语言: English
ISBN-10: 0198714939
ISBN-13: 9780198714934
Book Description
By finelybook
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.
Contents
List of Algorithms
List of Figures
Chapter 1: Mathematical foundations
Chapter 2: Optimization
Chapter 3: Random sampling
Chapter 4: Statistical modelling and inference
Chapter 5: Probabilistic graphical models
Chapter 6: Statistical machine learning
Chapter 7: Linear-Gaussian systems and signal processing
Chapter 8: Discrete signals: sampling,quantization and coding
Chapter 9: Nonlinear and non-Gaussian signal processing
Chapter 10: Nonparametric Bayesian machine learning and signal processing
Bibliography
Index
