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Artificial Intelligence: With an Introduction to Machine Learning, 2/Ed > 인공지능

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Artificial Intelligence: With an Introduction to Machine Learning, 2/Ed
히트도서
판매가격 39,000원
저자 Neapolitan
도서종류 외국도서
출판사 CRC
발행언어 영어
발행일 2018-5
페이지수 466
ISBN 9781138502383
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  • 도서 정보

    도서 상세설명

    Table of Contents

    1. Introduction to Artificial Intelligence
    1.1 History of Artificial Intelligence
    1.2 Outline of this Book

    Part I LOGICAL INTELLIGENCE

    2. Propositional Logic
    2.1 Basics of Propositional Logic
    2.2 Resolution
    2.3 Artificial Intelligence Applications
    2.4 Discussion and Further Reading

    3. First-Order Logic
    3.1 Basics of First-Order Logic
    3.2 Artificial Intelligence Applications
    3.3 Discussion and Further Reading

    4. Certain Knowledge Representation
    4.1 Taxonomic Knowledge
    4.2 Frames
    4.3 Nonmonotonic Logic
    4.4 Discussion and Further Reading

    5. Learning Deterministic Models
    5.1 Supervised Learning
    5.2 Regression
    5.3 Parameter Estimation
    5.4 Learning a Decision Tree

    PART II PROBABILISTIC INTELLIGENCE

    6. Probability
    6.1 Probability Basics
    6.2 RandomVariables
    6.3 Meaning of Probability
    6.4 RandomVariables in Applications
    6.5 Probability in the Wumpus World

    7. Uncertain Knowledge Representation
    7.1 Intuitive Introduction to Bayesian Networks
    7.2 Properties of Bayesian Networks
    7.3 Causal Networks as Bayesian Networks
    7.4 Inference in Bayesian Networks
    7.5 Networks with Continuous Variables
    7.6 Obtaining the Probabilities
    7.7 Large-Scale Application: Promedas

    8. Advanced Properties of Bayesian Network
    8.1 Entailed Conditional Independencies
    8.2 Faithfulness
    8.3 Markov Equivalence
    8.4 Markov Blankets and Boundaries

    9. Decision Analysis
    9.1 Decision Trees
    9.2 Influence Diagrams
    9.3 Modeling Risk Preferences
    9.4 Analyzing Risk Directly
    9.5 Good Decision versus Good Outcome
    9.6 Sensitivity Analysis
    9.7 Value of Information
    9.8 Discussion and Further Reading

    10. Learning Probabilistic Model Parameters
    10.1 Learning a Single Parameter
    10.2 Learning Parameters in a Bayesian Network .
    10.3 Learning Parameters with Missing Data

    11. Learning Probabilistic Model Structure
    11.1 Structure Learning Problem
    11.2 Score-Based Structure Learning
    11.3 Constraint-Based Structure Learning
    11.4 Application: MENTOR
    11.5 Software Packages for Learning
    11.6 Causal Learning
    11.7 Class Probability Trees
    11.8 Discussion and Further Reading

    12. Unsupervised Learning and Reinforcement Learning
    12.1 Unsupervised Learning
    12.2 Reinforcement Learning
    12.3 Discussion and Further Reading

    PART III EMERGENT INTELLIGENCE

    13. Evolutionary Computation
    13.1 Genetics Review
    13.2 Genetic Algorithms
    13.3 Genetic Programming
    13.4 Discussion and Further Reading

    14. Swarm Intelligence
    14.1 Ant System
    14.2 Flocks
    14.3 Discussion and Further Reading

    PART IV NEURAL INTELLIGENCE

    15. Neural Networks and Deep Learning
    15.1 The Perceptron
    15.2 Feedforward Neural Networks
    15.3 Activation Functions
    15.4 Application to Image Recognition
    15.5 Discussion and Further Reading

    PART V LANGUAGE UNDERSTANDING

    16. Natural Language Understanding
    16.1 Parsing
    16.2 Semantic Interpretation
    16.3 Concept/Knowledge Interpretation
    16.4 Information Extraction
    16.5 Discussion and Further Reading
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