Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and ApplicationsКНИГИ » ПРОГРАММИНГ
Название: Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications Автор: Herb Kunze, Davide La Torre, Adam Riccoboni Издательство: CRC Press Год: 2024 Страниц: 530 Язык: английский Формат: pdf (true) Размер: 40.0 MB
The fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams.
Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book.
A specific subset of AI is Machine Learning (ML). ML uses algorithms to learn from data to make future decisions or predictions. Machines are trained to solve problems without explicitly programming them to do so. Instead, the expression Deep Learning, denotes a specific subset of ML using artificial neural networks (ANN), which are layered structures inspired by the human brain. There are many different types of ML algorithms, but some of the most common include support vector machines (SVM), decision trees, ANN, and k-means clustering.
Deep Learning (DL) is an AI discipline and a type of ML technique aimed at developing systems that can operate in complex situations and focuses on Artificial Neural Networks. ANNs are networks composed of many interconnected processing nodes or neurons that can learn how to recognize complex patterns from data. ANNs are used for different applications, mostly for image recognition and classification, pattern recognition, and time series prediction. In Deep Learning, the so-called deep architectures are combinations of different ANNs.
Explainable and interpretable AI. Explainable AI (XAI) is AI in which the results of the solution can be understood and interpreted by humans. That is the ability to explain a model after it has been developed and providing transparent model architectures, which allows human users to both understand the data and trust results. This term is used in contrast with the concept of the ”black box” in ML in which even computer scientists and programmers cannot explain why an AI arrived at a specific decision.
This book is written for researchers, practitioners, engineers, and AI consultants.
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Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications 
Название: Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications
