An Introduction to Machine Learning [2nd ed.] / (Record no. 7048)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01787 a2200241 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250926125252.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 241204b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319639123 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Item number | KubM2 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Kubat, Miroslav |
| 245 ## - TITLE STATEMENT | |
| Title | An Introduction to Machine Learning [2nd ed.] / |
| Statement of responsibility, etc | Miroslav Kubat |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Switzerland: |
| Name of publisher | Springer; |
| Year of publication | ©2015 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xiii, 348p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.<br/><br/><br/>This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Computer Science |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial Intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Big Data/Analytics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Data Mining |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Knowledge Discovery |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Purchase Price | Bill number | Full call number | Accession Number | Copy number | Print Price | Bill Date/Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Computer Science | Indian Institute of Technology Tirupati | Indian Institute of Technology Tirupati | General Stacks | 30/07/2025 | Today and Tomorrows Printers and Publishers | 3526.94 | TTPP/133/2025-26 | 006.3 KubM2 (11542) | 11542 | Copy 1 | 5038.49 | 30/07/2025 | Books |