A Comprehensive Framework for Artificial Intelligence (eBook, ePUB)
A Synthesized Curriculum
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Finally, a single book that connects every piece of the AI puzzle, from core principles to the state of the art.This book offers a complete journey through the world of Artificial Intelligence. It starts with the absolute foundations, asking "What is AI?" and exploring its philosophical roots. You will learn to think about intelligence through the core concept of agents acting in environments. From there, we build the first problem-solving tools using state-space search. The book covers both uninformed and informed search strategies, like A*. It then moves into multi-agent environments with ad...
Finally, a single book that connects every piece of the AI puzzle, from core principles to the state of the art.
This book offers a complete journey through the world of Artificial Intelligence. It starts with the absolute foundations, asking "What is AI?" and exploring its philosophical roots. You will learn to think about intelligence through the core concept of agents acting in environments. From there, we build the first problem-solving tools using state-space search. The book covers both uninformed and informed search strategies, like A*. It then moves into multi-agent environments with adversarial search for games, using algorithms like Minimax. You'll learn how to represent problems declaratively with Constraint Satisfaction Problems. The curriculum then makes a major shift into knowledge representation, introducing formal logic as a tool for reasoning. After mastering propositional and first-order logic, the book confronts a crucial real-world challenge: uncertainty. It introduces probability theory, Bayesian networks, and models for reasoning over time, like Hidden Markov Models. The final parts dive into the machine learning revolution. You will get a thorough grounding in supervised learning, including regression, decision trees, and SVMs. You'll discover patterns in data with unsupervised learning techniques like k-Means and PCA. The journey culminates at the modern frontier, exploring the deep learning architectures that power today's AI. You'll understand Convolutional Neural Networks for vision, RNNs, and the powerful Transformer models for language.
What makes this book different is its synthesized and logical pathway. Instead of presenting a disconnected list of algorithms, it builds your understanding layer by layer, explaining why the field evolved as it did. It starts with a solid intellectual and computational bedrock, ensuring you grasp the core concepts of agency and systematic exploration before moving to more complex topics. It clearly explains the pivotal shifts in thinking-from the certainty of logic to the degrees of belief in probability, and from hand-crafted rules to learning directly from data. By tracing the progression from simple search to complex statistical methods, it reveals how the need to overcome computational complexity drove innovation. This narrative approach provides a deeper, more intuitive understanding of how these powerful ideas connect. The book doesn't just teach you how to build AI; it equips you to understand its impact and build it responsibly.
Disclaimer: This author has no affiliation with the board and it is independently produced under nominative fair use.
This book offers a complete journey through the world of Artificial Intelligence. It starts with the absolute foundations, asking "What is AI?" and exploring its philosophical roots. You will learn to think about intelligence through the core concept of agents acting in environments. From there, we build the first problem-solving tools using state-space search. The book covers both uninformed and informed search strategies, like A*. It then moves into multi-agent environments with adversarial search for games, using algorithms like Minimax. You'll learn how to represent problems declaratively with Constraint Satisfaction Problems. The curriculum then makes a major shift into knowledge representation, introducing formal logic as a tool for reasoning. After mastering propositional and first-order logic, the book confronts a crucial real-world challenge: uncertainty. It introduces probability theory, Bayesian networks, and models for reasoning over time, like Hidden Markov Models. The final parts dive into the machine learning revolution. You will get a thorough grounding in supervised learning, including regression, decision trees, and SVMs. You'll discover patterns in data with unsupervised learning techniques like k-Means and PCA. The journey culminates at the modern frontier, exploring the deep learning architectures that power today's AI. You'll understand Convolutional Neural Networks for vision, RNNs, and the powerful Transformer models for language.
What makes this book different is its synthesized and logical pathway. Instead of presenting a disconnected list of algorithms, it builds your understanding layer by layer, explaining why the field evolved as it did. It starts with a solid intellectual and computational bedrock, ensuring you grasp the core concepts of agency and systematic exploration before moving to more complex topics. It clearly explains the pivotal shifts in thinking-from the certainty of logic to the degrees of belief in probability, and from hand-crafted rules to learning directly from data. By tracing the progression from simple search to complex statistical methods, it reveals how the need to overcome computational complexity drove innovation. This narrative approach provides a deeper, more intuitive understanding of how these powerful ideas connect. The book doesn't just teach you how to build AI; it equips you to understand its impact and build it responsibly.
Disclaimer: This author has no affiliation with the board and it is independently produced under nominative fair use.
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