Artificial Intelligence With An Introduction To Machine Learning Second Edition by Richard E. Neapolitan, Taylor & Francis

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Taylor & Francis Artificial Intelligence With An Introduction To Machine Learning Second Edition by Richard E. Neapolitan

The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods._x000D__x000D__x000D_The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding._x000D__x000D__x000D_Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more._x000D_ _x000D_ 1. Introduction to Artificial Intelligence _x000D_ 1.1 History of Artificial Intelligence _x000D_ 1.2 Outline of this Book _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ Part I LOGICAL INTELLIGENCE _x000D_ _x000D_ _x000D_ 2. Propositional Logic _x000D_ 2.1 Basics of Propositional Logic _x000D_ 2.2 Resolution _x000D_ 2.3 Artificial Intelligence Applications _x000D_ 2.4 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 3. First-Order Logic _x000D_ 3.1 Basics of First-Order Logic _x000D_ 3.2 Artificial Intelligence Applications _x000D_ 3.3 Discussion and Further Reading _x000D_ _x000D_ _x000D_ 4. Certain Knowledge Representation _x000D_ 4.1 Taxonomic Knowledge _x000D_ 4.2 Frames _x000D_ 4.3 Nonmonotonic Logic _x000D_ 4.4 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 5. Learning Deterministic Models _x000D_ 5.1 Supervised Learning _x000D_ 5.2 Regression _x000D_ 5.3 Parameter Estimation _x000D_ 5.4 Learning a Decision Tree _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ PART II PROBABILISTIC INTELLIGENCE _x000D_ _x000D_ _x000D_ 6. Probability _x000D_ 6.1 Probability Basics _x000D_ 6.2 RandomVariables _x000D_ 6.3 Meaning of Probability _x000D_ 6.4 RandomVariables in Applications _x000D_ 6.5 Probability in the Wumpus World _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 7. Uncertain Knowledge Representation _x000D_ 7.1 Intuitive Introduction to Bayesian Networks _x000D_ 7.2 Properties of Bayesian Networks _x000D_ 7.3 Causal Networks as Bayesian Networks _x000D_ 7.4 Inference in Bayesian Networks _x000D_ 7.5 Networks with Continuous Variables _x000D_ 7.6 Obtaining the Probabilities _x000D_ 7.7 Large-Scale Application: Promedas _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 8. Advanced Properties of Bayesian Network _x000D_ 8.1 Entailed Conditional Independencies _x000D_ 8.2 Faithfulness _x000D_ 8.3 Markov Equivalence _x000D_ 8.4 Markov Blankets and Boundaries _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 9. Decision Analysis _x000D_ 9.1 Decision Trees _x000D_ 9.2 Influence Diagrams _x000D_ 9.3 Modeling Risk Preferences _x000D_ 9.4 Analyzing Risk Directly _x000D_ 9.5 Good Decision versus Good Outcome _x000D_ 9.6 Sensitivity Analysis _x000D_ 9.7 Value of Information _x000D_ 9.8 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 10. Learning Probabilistic Model Parameters _x000D_ 10.1 Learning a Single Parameter _x000D_ 10.2 Learning Parameters in a Bayesian Network . _x000D_ 10.3 Learning Parameters with Missing Data _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 11. Learning Probabilistic Model Structure _x000D_ 11.1 Structure Learning Problem _x000D_ 11.2 Score-Based Structure Learning _x000D_ 11.3 Constraint-Based Structure Learning _x000D_ 11.4 Application: MENTOR _x000D_ 11.5 Software Packages for Learning _x000D_ 11.6 Causal Learning _x000D_ 11.7 Class Probability Trees _x000D_ 11.8 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 12. Unsupervised Learning and Reinforcement Learning _x000D_ 12.1 Unsupervised Learning _x000D_ 12.2 Reinforcement Learning_x000D_ 12.3 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ PART III EMERGENT INTELLIGENCE _x000D_ _x000D_ _x000D_ 13. Evolutionary Computation _x000D_ 13.1 Genetics Review _x000D_ 13.2 Genetic Algorithms _x000D_ 13.3 Genetic Programming_x000D_ 13.4 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ 14. Swarm Intelligence _x000D_ 14.1 Ant System _x000D_ 14.2 Flocks _x000D_ 14.3 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ PART IV NEURAL INTELLIGENCE _x000D_ _x000D_ _x000D_ 15. Neural Networks and Deep Learning _x000D_ 15.1 The Perceptron _x000D_ 15.2 Feedforward Neural Networks _x000D_ 15.3 Activation Functions _x000D_ 15.4 Application to Image Recognition _x000D_ 15.5 Discussion and Further Reading _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ _x000D_ PART V LANGUAGE UNDERSTANDING _x000D_ _x000D_ _x000D_ 16. Natural Language Understanding _x000D_ 16.1 Parsing _x000D_ 16.2 Semantic Interpretation _x000D_ 16.3 Concept/Knowledge Interpretation _x000D_ 16.4 Information Extraction _x000D_ 16.5 Discussion and Further Reading_x000D_