Balancing strong theoretical foundations with system-level design considerations, the book serves as a structured guide for readers interested in how advanced mathematical models and learning paradigms drive modern AI-enabled IoT ecosystems.

The book begins with the mathematical, probabilistic, and computational principles underlying machine learning and visual intelligence, with subsequent chapters exploring linear and nonlinear models, kernel methods, neural networks, deep learning architectures, and optimisation techniques.

The integration of computer vision with IoT data pipelines, edge and cloud computing, wireless communication, and multi-agent systems is examined in detail. Advanced topics such as generative models, reinforcement learning, fuzzy intelligence, explainable AI, and real-world case studies demonstrate practical deployments in healthcare, smart environments, and autonomous systems.

Key Features:

-Balanced coverage of theoretical foundations and practical system design.
-Clear mathematical intuition supporting advanced learning and vision models.
-Covers emerging topics including explainable AI, generative models, and reinforcement learning.
-Application-driven case studies with integrated perspectives on machine learning, computer vision, and IoT technologies.