Artificial Intelligence Techniques and Optimization

G Kumaresan

ISBN 10: 9355287925 / ISBN 13: 9789355287922

Editore: MJP Publishers, IN, 2024

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Dati bibliografici

• Titolo: Artificial Intelligence Techniques and Optimization
• Editore: MJP Publishers, IN
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 9355287925
• ISBN 13: 9789355287922
• Rilegatura: Paperback
• Condizione: New
• Dimensioni: 13,97 L x 1,88 A x 21,59 P centimetri 13,97 centimetri di larghezza per 1,88 centimetri di altezza per 21,59 centimetri di profondità
• Peso: 434 grammi

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Riassunto

Artificial Intelligence: Techniques and Optimization by Dr. G. Kumaresan is an essential resource for students, professionals, and AI enthusiasts looking to understand and master the complex concepts of artificial intelligence. This well-structured textbook covers a wide range of AI topics, focusing on optimization techniques that enhance the efficiency and effectiveness of AI systems. Key features of the book include:

• Comprehensive Introduction to AI: The book starts by providing a solid foundation in the basics of artificial intelligence, discussing its history, development, and potential applications in today’s world.

• Diverse AI Environments: It explains the various types of AI environments, such as fully observable and partially observable environments, and provides insights into how intelligent agents operate in each type.

• Search Algorithms: A detailed explanation of uninformed and informed search algorithms, including Breadth-First Search (BFS), Depth-First Search (DFS), A* Search, and more. These algorithms are vital for solving complex problems in AI.

• Adversarial Search: The book explores adversarial search methods, including game theory approaches like Minimax and Alpha-Beta Pruning, which are essential for AI applications in competitive environments such as gaming.

• Learning Techniques: It covers different learning methods, including supervised, unsupervised, semi-supervised, and reinforcement learning, along with ensemble learning techniques for building more accurate and reliable AI models.

• Neural Networks: A section is dedicated to neural networks and how they are modeled after the human brain to process and learn from data.

• Optimization Algorithms: The book delves into optimization methods, discussing how AI systems can be fine-tuned using algorithms like Genetic Algorithms, Swarm Intelligence, and Evolutionary Computation.

• Practical Applications: Each chapter includes real-world examples and applications, making it easy for readers to understand how these AI techniques are applied in various industries.

This textbook is an ideal companion for anyone interested in learning AI from the ground up and those who wish to explore more advanced topics like optimization and machine learning algorithms.