Artificial Intelligence 1st edition by Peggy Thomas ISBN 1590184378  ‎ 978-1590184370

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ISBN 10: 1590184378
ISBN 13: ‎ 978-1590184370
Author: Peggy Thomas

This wide-ranging series explores environmental phenomena such as global warming, astronomical wonders such as black holes, and life-altering inventions such as lasers. Vivid, informative narrative and fully documented quotes enhance each volume in the series. All books include annotated bibliographies and an index.

Artificial Intelligence 1st Table of contents:

Preface

• Introduction to Artificial Intelligence
• Scope and Structure of the Book
• Evolution of AI: Past, Present, and Future

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Part I: Foundations of Artificial Intelligence

1. Introduction to Artificial Intelligence

   • What is AI?
   • History and Evolution of AI
   • AI vs. Human Intelligence
   • Types of AI: Narrow AI, General AI, and Superintelligence

2. Intelligent Agents and Environments

   • Definition and Components of Intelligent Agents
   • Types of Agents: Reactive, Deliberative, Hybrid
   • Environment Representation and Interaction
   • Rationality and Decision Making

3. Problem Solving and Search Algorithms

   • Problem Formulation and Representation
   • Search Algorithms: Uninformed vs. Informed
   • Breadth-First Search, Depth-First Search, A* Algorithm
   • Adversarial Search: Minimax and Alpha-Beta Pruning

4. Knowledge Representation

   • Symbols and Knowledge Structures
   • Propositional Logic and Predicate Logic
   • Semantic Networks and Frames
   • Ontologies and Knowledge Graphs

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Part II: Machine Learning and Data-Driven AI

5. Introduction to Machine Learning

   • What is Machine Learning?
   • Types of Learning: Supervised, Unsupervised, and Reinforcement
   • The Machine Learning Pipeline
   • Overfitting, Underfitting, and Model Evaluation

6. Supervised Learning

   • Regression and Classification Problems
   • Linear Regression, Logistic Regression
   • Decision Trees and Random Forests
   • Support Vector Machines (SVM)

7. Unsupervised Learning

   • Clustering Algorithms: K-Means, Hierarchical Clustering
   • Dimensionality Reduction: PCA and t-SNE
   • Anomaly Detection and Association Rule Mining

8. Reinforcement Learning

   • Basic Concepts: Agents, States, Actions, and Rewards
   • Markov Decision Processes (MDPs)
   • Q-Learning and Deep Q-Networks (DQN)
   • Policy Gradients and Actor-Critic Methods

9. Deep Learning

   • Neural Networks and Perceptrons
   • Deep Neural Networks (DNNs)
   • Convolutional Neural Networks (CNNs) for Image Processing
   • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
   • Generative Adversarial Networks (GANs)

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Part III: Advanced Topics in AI

10. Natural Language Processing (NLP)

    • Basics of NLP: Tokenization, Lemmatization
    • Language Models and Text Representation: TF-IDF, Word2Vec, BERT
    • Sentiment Analysis and Text Classification
    • Machine Translation and Speech Recognition

11. Computer Vision

    • Image Processing and Feature Extraction
    • Object Detection and Recognition
    • Convolutional Neural Networks (CNNs) in Vision
    • Image Segmentation and Style Transfer

12. AI Planning and Scheduling

    • Task Planning and Problem Solving
    • Classical Planning Algorithms: STRIPS, GraphPlan
    • Temporal and Hierarchical Planning
    • Constraint Satisfaction and Optimization

13. Robotics and Autonomous Systems

    • Introduction to Robotics: Sensors, Actuators, and Control
    • Path Planning and Navigation
    • SLAM (Simultaneous Localization and Mapping)
    • Multi-Agent Systems and Coordination

14. Cognitive Architectures

    • Cognitive Models of Human Thought
    • Architectures: SOAR, ACT-R, and LIDA
    • Cognitive Robotics and Human-AI Interaction
    • Applications of Cognitive Architectures in AI Systems

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Part IV: Ethics, Safety, and Future of AI

15. Ethics and Bias in AI

    • Ethical Challenges in AI Development
    • Bias in AI Algorithms and Data
    • Fairness, Accountability, and Transparency
    • Ethical Guidelines and Regulation for AI

16. AI Safety and Control

    • The Control Problem and Friendly AI
    • Ensuring AI Alignment with Human Values
    • Risks of Superintelligent AI and Safeguards
    • Approaches to Value Alignment and AI Governance

17. Artificial General Intelligence (AGI)

    • Theories of AGI
    • Progress Toward AGI: Current State and Challenges
    • The Singularity and its Implications
    • AGI Safety and the Future of AI

18. Applications of AI

    • AI in Healthcare, Finance, and Education
    • Autonomous Vehicles and Smart Cities
    • AI in Entertainment: Gaming and Media
    • AI in Scientific Research and Innovation

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Conclusion

• Summary of Key Concepts
• The Future of AI: Opportunities and Challenges
• Final Thoughts: AI’s Impact on Society

References

• Cited Works and Further Reading

Index

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