Mathematical Methodologies in Pattern Recognition and Machine Learning

The book builds on an analogy between social groups and assemblies of molecules to introduce the concepts of statistical mechanics, machine learning and data science. The idea is to bolster students' interest by proposing a new take on the topic that draws on a data analytics approach of molecules. Specifically, how the analysis of their individual features, as well as of interactions and "communication" processes can predict their properties and collective behavior, just as polling and social networking shed light on the behavior of social groups. Applications to systems at the cutting-edge of research e.g. environmental and energy applications, will be used.Key Features: Draws on a data analytics approach of molecules Covers hot topics such as artificial intelligence and machine learning of molecular trends Applications to systems at the cutting-edge of research e.g. environmental and energy applications, are used Discusses molecular simulation and links with other important, emergin

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This

Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the included MATLAB and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers develop predictive models through AI/ML algorithms by writing their own computer code or usin

Timely and reliable information on natural resources, regarding their potential and limitations, is a prerequisite for sustainable development. Geospatial technologies offer immense potential in providing such information in a timely and cost-effective manner. Using orbital sensors data in conjunction with airborne and proximal sensors to generate information on soils and agricultural resources, forests, mineral resources, fossil fuel, wetlands, water resources, and marine resources, this book focuses on the advancements in technologies applicable to managing these resources. It addresses global issues like climate change and land degradation neutrality and introduces Spatial Data Infrastructure (SDI) as a mechanism for sharing geospatial data. It also provides in-depth discussion on drones, crowdsourcing, cloud computing, Internet of Things, machine learning, and their applications.Features Provides a comprehensive resource on the latest developments in geospatial technologies and the