Artificial Intelligence : A Textbook / by Charu C. Aggarwal.

Format:

Book

Author/Creator:

Aggarwal, Charu C., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Data mining.

Artificial Intelligence.

Machine Learning.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XX, 483 pages) : 173 illustrations, 15 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories: Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5. Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11. Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence. The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.

Contents:

1 An Introduction to Artificial Intelligence

2 Searching State Spaces

3 Multiagent Search

4 Propositional Logic

5 First-Order Logic

6 Machine Learning: The Inductive View

7 Neural Networks

8 Domain-Specific Neural Architectures

9 Unsupervised Learning

10 Reinforcement Learning

11 Probabilistic Graphical Models

12 Knowledge Graphs

13 Integrating Reasoning and Learning.

Other Format:

Printed edition:

ISBN:

978-3-030-72357-6

9783030723576

Access Restriction:

Restricted for use by site license.