AI Foundations and Applications with MATLAB
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 7 September 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031844225
- Binding Paperback
- No. of pages664 pages
- Size 235x155 mm
- Language English
- Illustrations XXI, 664 p. 855 illus., 570 illus. in color. Illustrations, black & white 700
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Long description:
This textbook provides fundamentals and practical skills on AI foundations and applications with two MATLAB programming modes. It includes twelve chapters with detailed introductions for the foundation knowledge of AI, structures, key components, and hands-on AI projects implemented in various applications in our world.
Unlike other AI related textbooks, in which the Python is used, the MATLAB is adopted in this textbook. The Python programming mode builds AI projects with functions involving huge blocks of codes, which is a difficult task. However, in MATLAB mode, provides two programming styles, Apps, and function library. The Apps graphical user interface (GUIs) assist users, especially the beginners, to learn and build AI projects with no coding lines quickly and easily. To compensate the possible code-hiding in Apps, MATLAB provides a Converting Codes function to allow users to convert those Apps to the related codes. It enables users to have a clear picture between Apps and detailed coding process. The function library enables users to build AI projects with detailed codes. This textbook also includes homework questions, exercises, lab projects and case studies.
This book is designed as a textbook for advanced-level students in Computer Science or Computer Engineering. Also, AI engineers, who have an interest in learning and developing professional AI applications to solve real problems in the world will want to purchase this book.
MoreTable of Contents:
Chapter 1 Introduction.- Chapter 2 Learning and Decision Making Process.- Chapter 3 Fuzzy Logic Inference System.- Chapter 4 Introduction to Machine Learning.- Chapter 5 Introduction to Regression Algorithms.- Chapter 6 Introduction to Classification Algorithms.- Chapter 7 Neural Networks and Deep Learning.- Chapter 8 Introduction to Unsupervised Learning.- Chapter 9 Introduction to Reinforcement Learning.- Chapter 10 Introduction to Adaptive Neuro Fuzzy Inference System.- Chapter 11 Case Study Projects on Fuzzy Logic Technology.- Chapter 12 Case Study Projects on Deep Learning.- Appendix A.
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