Artificial intelligence : (Record no. 13994)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02183cam a2200193Ii 4500 |
| INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783030723576 |
| INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 3030723577 |
| DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | Q335 .A34 2021 |
| Item number | 1 |
| MAIN ENTRY--AUTHOR NAME | |
| Personal name | Aggarwal, Charu C., |
| TITLE STATEMENT | |
| Title | Artificial intelligence : |
| Remainder of title | a textbook / |
| Statement of responsibility, etc | Charu C. Aggarwal. |
| Copyright Date | |
| Place of publication | Cham : |
| Name of publisher | Springer, |
| Year of publication or production | [2021] |
| Copyright Date | |
| Year of publication or production | ©2021 |
| PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource : |
| Other physical details | illustrations (some color) |
| FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| SUMMARY, ETC. | |
| Summary, etc | 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. |
| SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence. |
| SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence. |
| ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Library of Congress Classification |
| Koha item type | Books |
| Permanent Location | Current Location | Date acquired | Full call number | Accession Number | Koha item type |
|---|---|---|---|---|---|
| Gabriel Afolabi Ojo Central Library (Headquarters). | Gabriel Afolabi Ojo Central Library (Headquarters). | 11/05/2024 | Q335 .A34 2021 | 0194487 | Books |