Classical Statistical Mechanics

Classical Statistical Mechanics

by Michael Robert

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ISBN 9781836592693
Publisher Chapman Press
Copyright Year 2025
Price £171.00

About This Book

Artificial Intelligence: A Modern Approach is a comprehensive guide to the field of AI, offering both theoretical and practical insights into the development of intelligent systems. The book emphasizes the interdisciplinary nature of AI, combining computer science, mathematics, cognitive science, and engineering to address complex problems. At the heart of the book is the exploration of intelligent agents-systems that perceive their environment, make decisions, and act to achieve goals. These agents are studied within frameworks such as problem-solving, knowledge representation, reasoning, learning, and natural language processing. A strong focus is placed on algorithms and techniques, including search algorithms, decision trees, and neural networks, which form the backbone of AI applications. The book also delves into advanced topics like robotics, planning, and uncertainty management, illustrating how AI systems handle real-world challenges. Ethical considerations are explored, highlighting issues like fairness, transparency, and the societal implications of AI deployment. By balancing foundational theories with practical applications, Artificial Intelligence: A Modern Approach equips readers with the knowledge to understand and develop AI systems. It is an essential resource for students, researchers, and professionals aiming to deepen their understanding of this transformative field and its potential to reshape industries and society. Artificial Intelligence: A Modern Approach is a definitive resource that explores the principles, algorithms, and applications of AI, blending theory and practice. Contents: 1. Artificial Intelligence: Current Technological Landscape, 2. AI Intelligent Agents: Attributes and Practical Applications, 3. Artificial Intelligence in Automated Trading, 4. Client-Server Systems and Virtual Machine Integration, 5. Gradient Descent in Machine Learning, 6. Exploring Hierarchies of Intelligence Analysis, 7. Challenges in Complex Knowledge Representation, 8. Machine Learning in Robotics through Artificial Intelligence.