Zhen "Leo" Liu

Artificial Intelligence for Engineers (eBook, PDF)

Basics and Implementations

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 439
• Erscheinungstermin: 3. Januar 2025
• Englisch
• ISBN-13: 9783031759536
• Artikelnr.: 72725938

Autorenporträt

Leo Liu's research interests include poromechanics, multiphysics, Artificial Intelligence (AI), and cyber-physical systems. The scope covers both traditional civil engineering applications, such as unsaturated soil mechanics, geohazards, energy geotechnics, and more interdisciplinary innovations, such as intelligent geosystems (for smart cities), system resilience against disaster and weather, and "big data" solutions for intelligent geosystems and transportation systems. His research has many direct applications in infrastructure safety, energy resources, environment protection, and advanced materials.

Inhaltsangabe

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.