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.

Offers fundamental theoretical knowledge to master the main AI topics with two MATLAB programming modes Provides teaching materials to assist instructors and students to teach and learn AI technologies efficiently Includes 134 projects, class and lab projects, homework questions and all sample datasets used to build lab projects Request lecturer material: sn.pub/lecturer-material

Autorentext

Dr. YING BAI is a Professor in the Department of Computer Science and Engineering at Johnson C. Smith University located at Charlotte, North Carolina. His special interests include: Artificial intelligence, soft-computing, database programming, fuzzy logic controls, automatic and robots controls, as well as robots calibrations. His industry experience includes positions as software and senior software engineers at companies such as Motorola MMS, Schlumberger ATE Technology, Immix TeleCom, and Lam Research. Since 2003, Dr. Bai has published 20 books with publishers such as Prentice Hall, CRC Press LLC, Springer, Cambridge University Press and Wiley IEEE Press. He also published more than 70 academic research papers in IEEE Trans. journals and international conferences. Most books are about the micro-controller control and programming, fuzzy logic implementations, classical and modern controls, cross-language interface programming, database programming and applications.

Inhalt

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.