Neapolitan / Jiang Contemporary Artificial Intelligence, Second Edition

With an Introduction to Machine Learning, Second Edition
2. Auflage 2018
ISBN: 978-1-351-38438-4
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 480 Seiten

Reihe: Chapman & Hall/CRC Artificial Intelligence and Robotics Series

This book presents AI methods and algorithms for solving challenging problems within systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, etc.

Autoren/Hrsg.

Neapolitan, Richard E.

Jiang, Xia

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

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

Über Autor(innen)

Richard E. Neapolitan is a professor of computer science at Northeastern Illinois University. Dr. Neapolitan has published in prestigious journals related to reasoning under uncertainty and has authored five books: Probabilistic Reasoning in Expert Systems, Learning Bayesian Networks, Foundations of Algorithms, Probabilistic Methods for Financial and Marketing Informatics, and Probabilistic Methods for Bioinformatics. His research interests include probability and statistics, expert systems, cognitive science, and applications of probabilistic modeling to fields such as medicine, biology, and finance.

Xia Jiang is an assistant professor in the Department of Biomedical Informatics at the University of Pittsburgh. Dr. Jiang coauthored Probabilistic Methods for Financial and Marketing Informatics. Her current research focuses on developing novel algorithms/systems that improve the computational efficiency of large data analysis and network modeling of cancer genome data.