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Artificial intelligence for engineers = basics and implementations /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence for engineers/ by Zhen "Leo" Liu.
Reminder of title:	basics and implementations /
Author:	Liu, Zhen.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiii, 439 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Preparation Knowledge: Basics of AI -- Tools for Artificial Intelligence -- Linear Models -- Decision Trees -- Support Vector Machine -- Bayesian Algorithms -- Artificial Neural Network -- Deep Learning -- Ensemble Learning -- Clustering -- Dimension Reduction -- Anomaly Detection -- Association Rule Leaming -- Basics of and Value-Based Reinforcement Learning -- Policy-Based Reinforcement Learning.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-031-75953-6
ISBN:	9783031759536

Artificial intelligence for engineers = basics and implementations /
Liu, Zhen.

Artificial intelligence for engineersbasics and implementations /[electronic resource] :by Zhen "Leo" Liu. - Cham :Springer Nature Switzerland :2025. - xiii, 439 p. :ill. (chiefly color), digital ;24 cm.

Preparation Knowledge: Basics of AI -- Tools for Artificial Intelligence -- Linear Models -- Decision Trees -- Support Vector Machine -- Bayesian Algorithms -- Artificial Neural Network -- Deep Learning -- Ensemble Learning -- Clustering -- Dimension Reduction -- Anomaly Detection -- Association Rule Leaming -- Basics of and Value-Based Reinforcement Learning -- Policy-Based Reinforcement Learning.

This textbook 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 a big picture for both AI and essential AI topics within the shortest amount of time. Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar; Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms; Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI. Extra information is available at AI-engineer.org.

ISBN: 9783031759536

Standard No.: 10.1007/978-3-031-75953-6doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: TA347.A78

Dewey Class. No.: 670.28563

Artificial intelligence for engineers = basics and implementations /
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505 0 $a Preparation Knowledge: Basics of AI -- Tools for Artificial Intelligence -- Linear Models -- Decision Trees -- Support Vector Machine -- Bayesian Algorithms -- Artificial Neural Network -- Deep Learning -- Ensemble Learning -- Clustering -- Dimension Reduction -- Anomaly Detection -- Association Rule Leaming -- Basics of and Value-Based Reinforcement Learning -- Policy-Based Reinforcement Learning.
520 $a This textbook 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 a big picture for both AI and essential AI topics within the shortest amount of time. Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar; Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms; Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI. Extra information is available at AI-engineer.org.
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