Artificial intelligence is no longer just a collection of algorithms-it is an engineering discipline concerned with building systems that can reason, learn, perceive, adapt, and operate responsibly in the real world.
Engineering Intelligent Systems provides a structured, systems-oriented introduction to modern artificial intelligence, connecting foundational theory with the practical decisions involved in designing and deploying intelligent systems. The book begins with AI foundations and progresses through search, knowledge representation, planning, and reasoning under uncertainty before moving into supervised and unsupervised learning, neural networks, natural language processing, computer vision, reinforcement learning, expert systems, and generative AI.
Rather than treating these topics as isolated techniques, the book emphasizes how they fit together within complete intelligent systems. Later chapters address ethics, responsible AI, MLOps, deployment, monitoring, and the future of AI engineering-helping readers understand not only how intelligent models are built, but how they are evaluated, integrated, governed, and maintained.
Designed for advanced undergraduate and graduate students, instructors, software engineers, data professionals, and technical practitioners, Engineering Intelligent Systems balances conceptual clarity with engineering judgment and real-world relevance. It is a practical foundation for readers who want to move from understanding AI methods to engineering AI systems.
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