[PDF] Engineering Mathematics And Artificial Intelligence eBook

Author : Herb Kunze
Publisher : CRC Press
Page : 530 pages
File Size : 21,84 MB
Release : 2023-07-26
Category : Technology & Engineering
ISBN : 1000907872

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Explains the theory behind Machine Learning and highlights how Mathematics can be used in Artificial Intelligence Illustrates how to improve existing algorithms by using advanced mathematics and discusses how Machine Learning can support mathematical modeling Captures how to simulate data by means of artificial neural networks and offers cutting-edge Artificial Intelligence technologies Emphasizes the classification of algorithms, optimization methods, and statistical techniques Explores future integration between Machine Learning and complex mathematical techniques

Author : D. Jude Hemanth
Publisher : Springer Nature
Page : 1105 pages
File Size : 30,69 MB
Release : 2020-01-03
Category : Technology & Engineering
ISBN : 3030361780

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This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20–22 April 2019 at Antalya, Manavgat (Turkey). In today’s world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
Page : 392 pages
File Size : 50,64 MB
Release : 2020-04-23
Category : Computers
ISBN : 1108569323

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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Author : Steven L. Brunton
Publisher : Cambridge University Press
Page : 615 pages
File Size : 14,70 MB
Release : 2022-05-05
Category : Computers
ISBN : 1009098489

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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Author : T. Ananth Kumar
Publisher : John Wiley & Sons
Page : 370 pages
File Size : 40,95 MB
Release : 2021-08-16
Category : Mathematics
ISBN : 1119785502

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SIMULATIONS AND ANALYSIS of Mathematical Methods Written and edited by a group of international experts in the field, this exciting new volume covers the state of the art of real-time applications of computer science using mathematics. This breakthrough edited volume highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers. The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientist’s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

Author : Park Gyei-Kark
Publisher : Springer Nature
Page : 303 pages
File Size : 31,89 MB
Release : 2022-10-03
Category : Mathematics
ISBN : 9811923000

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This book contains select papers presented at the 3rd International Conference on Engineering Mathematics and Computing (ICEMC 2020), held at the Haldia Institute of Technology, Purba Midnapur, West Bengal, India, from 5–7 February 2020. The book discusses new developments and advances in the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, hybrid intelligent systems, etc. The book, containing 19 chapters, is useful to the researchers, scholars, and practising engineers as well as graduate students of engineering and applied sciences.

Author : Edward A. Bender
Publisher : Wiley-IEEE Computer Society Press
Page : 0 pages
File Size : 16,6 MB
Release : 1996-02-10
Category : Technology & Engineering
ISBN : 9780818672002

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Mathematical Methods in Artificial Intelligence introduces the student to the important mathematical foundations and tools in AI and describes their applications to the design of AI algorithms. This useful text presents an introductory AI course based on the most important mathematics and its applications. It focuses on important topics that are proven useful in AI and involve the most broadly applicable mathematics. The book explores AI from three different viewpoints: goals, methods or tools, and achievements and failures. Its goals of reasoning, planning, learning, or language understanding and use are centered around the expert system idea. The tools of AI are presented in terms of what can be incorporated in the data structures. The book looks into the concepts and tools of limited structure, mathematical logic, logic-like representation, numerical information, and nonsymbolic structures. The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge. The main tools for manipulating knowledge nonsymbolically, as neural nets, are optimization methods and statistics. This material is covered in the text by topics such as trees and search, classical mathematical logic, and uncertainty and reasoning. A solutions diskette is available, please call for more information.

Author : National Research Council
Publisher : National Academies Press
Page : 145 pages
File Size : 17,3 MB
Release : 1991-02-01
Category : Technology & Engineering
ISBN : 0309046483

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Computational mechanics is a scientific discipline that marries physics, computers, and mathematics to emulate natural physical phenomena. It is a technology that allows scientists to study and predict the performance of various productsâ€"important for research and development in the industrialized world. This book describes current trends and future research directions in computational mechanics in areas where gaps exist in current knowledge and where major advances are crucial to continued technological developments in the United States.

Author : Kaushik Kumar
Publisher : CRC Press
Page : 157 pages
File Size : 40,82 MB
Release : 2021-06-20
Category : Computers
ISBN : 1000396932

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Artificial Intelligence in Mechanical and Industrial Engineering offers a unified platform for the dissemination of basic and applied knowledge on the integration of artificial intelligence within the realm of mechanical and industrial engineering. The book covers the tools and information needed to build successful careers and a source of knowledge for those working with AI within these domains. The book offers a systematic approach to explicate fundamentals as well as recent advances. It incorporates various case studies for major topics as well as numerous examples. It will also include real-time intelligent automation and associated supporting methodologies and techniques, and cover decision-support systems, as well as applications of Chaos Theory and Fractals. The book will give scientists, researchers, instructors, students, and practitioners the tools and information needed to build successful careers and to be an impetus to advancements in next-generation mechanical and industrial engineering domains.

Author : Kuldeep Singh
Publisher : Bloomsbury Publishing
Page : 944 pages
File Size : 47,16 MB
Release : 2019-12-13
Category : Technology & Engineering
ISBN : 0230345980

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This popular, world-wide selling textbook teaches engineering mathematics in a step-by-step fashion and uniquely through engineering examples and exercises which apply the techniques right from their introduction. This contextual use of mathematics is highly motivating, as with every topic and each new page students see the importance and relevance of mathematics in engineering. The examples are taken from mechanics, aerodynamics, electronics, engineering, fluid dynamics and other areas. While being general and accessible for all students, they also highlight how mathematics works in any individual's engineering discipline. The material is often praised for its careful pace, and the author pauses to ask questions to keep students reflecting. Proof of mathematical results is kept to a minimum. Instead the book develops learning by investigating results, observing patterns, visualizing graphs and answering questions using technology. This textbook is ideal for first year undergraduates and those on pre-degree courses in Engineering (all disciplines) and Science. New to this Edition: - Fully revised and improved on the basis of student feedback - New sections - More examples, more exam questions - Vignettes and photos of key mathematicians