The book aims to equip engineers, data scientists, and students with the foundational knowledge required to delve deeper into the AI field. It explains the mathematics behind AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, and optimal control. It also provides guidance on how to adapt these mathematical methods to different applications from various fields, and helps readers gain the mathematical fluency to interpret and explain how AI systems make decisions.
Key takeaways:
- Understanding the underlying mathematics powering AI systems is crucial to build successful solutions.
- Mathematical topics critical for AI include regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more.
- Adapting mathematical methods to different applications from various fields is a necessary skill in AI.
- Gaining mathematical fluency helps to interpret and explain how AI systems arrive at their decisions.