Название: Artificial Intelligence for Engineers: Basics and Implementations
Автор: Zhen "Leo" Liu
Издательство: Springer
Год: 2025
Страниц: 441
Язык: английский
Формат: pdf (true)
Размер: 34.0 MB

This book presents basic knowledge and essential toolsets needed for people who want to step into Artificial Intelligence (AI). The book is especially suitable for those college students, graduate students, instructors, and IT hobbyists who have an engineering mindset. That is, it serves the idea of getting the job done quickly and neatly with an adequate understanding of why and how. It is designed to allow one to obtain big pictures for both AI and essential AI topics within the shortest amount of time. Based on the picture(s), suitable amounts of theoretical knowledge are contextualized to help the learner gain information about the most essential concepts and algorithms. These algorithms are introduced and formulated in a way that the learner can easily implement them for real-world applications with a small amount of effort. In short, you read it, you understand it, you try it, and you can solve it.

This book, though titled Artificial Intelligence, is mostly devoted to numeric AI represented by Machine Learning algorithms, which predominate the so-called third wave/tide of AI. The most common and useful Machine Learning topics are selected and introduced. This includes introductions to topics that are needed to gain a basic understanding of Machine Learning, such as linear models, decision trees, Bayesian algorithms, and clustering algorithms, as well as more advanced topics like Deep Learning and Reinforcement Learning. It attempts to cover the essential terms, basic/common algorithms, and useful tools that one may encounter in a typical journey for learning and performing contemporary AI.

The book aims to strike a balance between being pragmatic and theoretical. Many AI learners, especially those in engineering applications, tend to solve a problem as quickly as possible, for example, using some AI code or libraries from the Internet. This usually works well considering the maturity of many AI tools. However, it may lead to unnecessary, inappropriate use of such tools and hinder further learning of the topics. On the contrary, some other learners try to start with intricate math and bottom-level Computer Science knowledge, which can easily discourage them and eventually turn out to be not needed in many cases. Each of the topics (or chapters) in this book adopts its own storyline, which may share a commonality with other topics while still maintaining uniqueness that stems from its own historical development and algorithmic nature.

- Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar
- Organized to help readers to easily and quickly gain the most essential concepts, algorithms and their implementations
- Covers essential background knowledge, algorithms, tools, and code for learning and performing contemporary AI

Contents:







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