Essential Math for AI

Next-Level Mathematics for Developing Efficient and Successful AI Systems

Essential Math for AI
Hala Nelson
RRP:
NZ$ 142.99
Our Price:
NZ$ 121.54
Paperback
h233 x 178mm - 525pg
31 Dec 2022 US
International import eta 7-19 days
9781098107635
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Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you' re just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
Hala Nelson is an Associate Professor of Mathematics at James Madison University. She has a Ph. D. in Mathematics from the Courant Institute of Mathematical Sciences at New York University. Before James Madison University, she was a postdoctoral Assistant Professor at the University of Michigan- Ann Arbor. Her research is in the areas of Materials Science, Statistical Mechanics, Inverse Problems, and the mathematics of Machine Learning and Artificial Intelligence. She enjoys translating complex ideas into simple and practical terms, and believes that most mathematical concepts become painless and relatable when presented within the right context. This book is the result of three courses she regularly teaches: Mathematics and Artificial Intelligence, Optimization, and Computers and Numerical Algorithms.

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