MATLAB® for Scientific Computing and Artificial Intelligence: Chapman & Hall/CRC Mathematics and Artificial Intelligence Series
Autor Stephen Lynchen Limba Engleză Paperback – 3 feb 2027
This book was developed from a series of national and international workshops that the author has been delivering for over twenty-five years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.
Features:
- No prior experience of programming is required.
- Online GitHub repository available with codes for readers to practice.
- Covers applications and examples from biology, chemistry, computer science, data science, earth sciences, economics, electrical and mechanical engineering, mathematics, physics, psychology, sports science, statistics, and neuron oscillator computing.
- Full solutions to exercises are available on the Web.
GitHub Repository of MATLAB/Simulink files, data files, and Solutions to Exercises:
https://github.com/proflynch/MATLAB-for-Scientific-Computing-and-AI/
MATLAB Central File Exchange:
https://mathworks.com/matlabcentral/profile/authors/63144/
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Specificații
ISBN-13: 9781041111160
ISBN-10: 1041111169
Pagini: 392
Ilustrații: 312
Dimensiuni: 178 x 254 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Mathematics and Artificial Intelligence Series
ISBN-10: 1041111169
Pagini: 392
Ilustrații: 312
Dimensiuni: 178 x 254 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Mathematics and Artificial Intelligence Series
Public țintă
AS/A2 and Undergraduate CoreCuprins
Section 1: An Introduction to MATLAB 1. Getting Started with MATLAB 2. MATLAB for Scientific Computing 3. MATLAB for AS-Level (High School Mathematics 4. MATLAB for A-Level (High School Mathematics 5. Differential Equations Section 2: MATLAB for Scientific Computing 6. Biology 7. Chemistry 8. Computer Science 9. Cryptography 10. Data Science 11. Earth Sciences 12. Economics 13. Engineering 14. Fractals and Multifractals 15. Image Processing 16. Numerical Methods for Ordinary and Partial Differential Equations 17. Physics 18. Psychology 19. Sports Science 20. Statistics 21. Simulink Section 3: Artificial Intelligence 22. Neuron Oscillator Computing 23. Neural Networks and Neurodynamics 24. The Deep Learning Toolbox 25. Convolutional Neural Networks
Notă biografică
Stephen Lynch is a Professor of Digital Skills at Loughborough University, UK. According to the scholarly analytics platform ScholarGPS®, he is currently ranked #13 in the world for Dynamical Systems. He is an author of MapleTM, MATLAB®, Mathematica®, and Python books. In 2022, he was named a National Teaching Fellow, which celebrates and recognises individuals who have made an outstanding impact on student outcomes and teaching in higher education. It is the highest teaching award in the UK. He won the award for his work in programming in the STEM subjects, research feeding into teaching, and widening participation (using experiential and object-based learning). Although educated as a pure mathematician, Stephen’s many interests now include applied mathematics, artificial intelligence, cell biology, electrical engineering, computing, neural networks, nonlinear optics, and binary oscillator computing, which he co-invented with a colleague. He has authored 2 international patents for inventions, 11 books, 4 book chapters, over 50 journal articles, and a few conference proceedings. Stephen is a Fellow of the Institute of Mathematics and Its Applications (FIMA) and a Senior Fellow of the Higher Education Academy (SFHEA). In 2010, Stephen volunteered as a STEM Ambassador, in 2012, he was awarded Public Engagement Champion status, and in 2014 he became a Speaker for Schools. He runs national workshops on "Python for A-Level Mathematics and Beyond," and international workshops on "Python for Scientific Computing and TensorFlow for Artificial Intelligence." He has run workshops in China, Malaysia, Saudi Arabia, Singapore, and the USA.
Descriere
This book is split into 3 parts: Section I serves as an introduction to MATLAB, Section II delves into how it can be used to solve a wide range of real-world problems and introduces Simulink, and Section III explores neuron oscillator computing, neural networks, and how the Deep Learning Toolbox can help solve problems in AI.