دانلود کتاب پردازش سیگنال و نظریه یادگیری ماشین

Signal processing and machine learning are vital areas of knowledge that find applications in virtually all aspects of modern life. Indeed, human beings have been employing signal processing tools for centuries without being aware of them. In recent years the affordable availability of computational and storage resources, in addition to theoretical advances, allowed a booming activity in both related areas. In present days the younger generation might not be able to understand how one can live without carrying a mobile phone, travel long distances without an almost self-piloted airplane, explore other planets without human presence, and utilize a medical facility without a wide range of diagnostic and intervention equipment. Signal processing consists of mapping or transforming information-bearing signals into another form of signals at the output, aiming at some application benefits. This mapping defines a continuous or analog system if it involves functions representing the input and output signals. On the other hand, the system is discrete or digital if sequences of numbers represent its input and output signals. Signal processing theory includes a wide range of tools which have found applications in uncountable areas, such as bioengineering, communications, control, surveillance, environment monitoring, oceanography, and astronomy, to mention a few. Many of these applications benefited from machine learning theory by solving some unprecedented problems and enhancing existing solutions. Machine learning basically consists of fitting models to the provided data. There are many algorithms to perform the desired fitting, but it is worth mentioning that data quality is essential for a successful outcome. In addition, the book includes many topics on machine learning, highlighting some connections with many classical methods related to learning from data. Indeed, machine learning is a framework for many disciplines dealing with tools to learn from data and perform modeling tasks such as prediction and classification. This book includes an overview of classical signal processing and machine learning tools, whose basic concepts are encountered in numerous textbooks available in the literature, as well as topics usually not covered in the books available in the market. As a result, we believe that students and professionals can benefit from reading many parts of this book, if not all, in order to deepen and widen their current knowledge as well as to start exploiting new ideas. Another objective of this book is to offer a wide range of self-contained topics in signal processing and machine learning which can be used as complementary to existing standard courses and, above all, serve as the basis for advanced courses. The breadth of subjects discussed provides ample coverage, so that many configurations are possible for an entire course on special topics in signal processing and machine learning according to the selection of chapters. The broad scope of topics is inspired by the fact that several solutions for practical problems start by acquiring some continuous-time signals, followed by digitizing and storing them to perform further processing. The set of tasks may entail signal filtering, modeling, prediction, and classification, among others. The theories of signal processing and machine learning have been developing for over five decades at a fast pace. The following chapters will cover many of the classical tools widely employed in applications that we perceive in our daily life through the use of mobile phones, media players, medical equipment, transportation, and so on. These chapters will also cover recent advances and describe many new tools that can potentially be utilized in new applications and further investigated. The authors of the chapters are frequent and vital contributors to the fields of signal processing and machine learning theory.