Engineering Artificial Intelligence Foundations, Algorithms, Machine Learning, Deep Learning, and Intelligent Systems
EMRAH EKREM KARABAG · Zenodo (CERN European Organization for Nuclear Research) · 2026
Artificial Intelligence has evolved from a specialized area of computer science into a foundational engineering discipline influencing aerospace, autonomous systems, telecommunications, automotive engineering, manufacturing, cybersecurity, robotics, predictive maintenance, and Digital Twins. Understanding AI requires more than training a model or using a Machine Learning library. Engineers must understand the complete lifecycle: Mathematics → Data → Algorithm → Training → Validation → Deployment → Monitoring A high-performing model may still fail in operation because of poor data quality, environmental changes, unexpected inputs, latency constraints, misunderstood confidence levels, or inadequate safety mechanisms. For this reason, this book approaches Artificial Intelligence from an engineering perspective. Its central question is: How can Artificial Intelligence be engineered into reliable technical systems? The chapters progress from mathematical foundations and Machine Learning to neural networks, Deep Learning, Transformers, Large Language Models, Reinforcement Learning, Generative AI, explainability, safety, and AI Systems Engineering. Mathematical concepts are connected with practical implementations and engineering examples. A central distinction throughout the book is between: Model Prediction and Operational Decision In safety-related systems, an AI prediction alone may not be sufficient. Operational decisions may also require physical validation, redundant sensing, uncertainty analysis, independent monitoring, and deterministic safety logic. Several engineering principles guide the discussion: Data Quality: Reliable AI begins with reliable data. Generalization: Operational performance matters more than training accuracy. Complexity: More sophisticated models are not always better; simpler models may provide lower latency, easier validation, and stronger interpretability. Uncertainty: Confidence should never be confused with certainty. Verified Operational Authority: AI capability should remain within validated operational limits, particularly in high-consequence systems. The book is intended for students, researchers, software developers, and engineers seeking to bridge: Academic Theory → Engineering Practice and AI Algorithm → AI-Enabled System Readers are encouraged not only to implement AI algorithms, but also to examine their assumptions, limitations, uncertainty, and behavior outside ideal laboratory conditions. Artificial Intelligence technologies will continue to change rapidly. The mathematical foundations and engineering principles required to evaluate and deploy them responsibly will remain essential.