Fundamentals of machine learning (Record no. 796)
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| fixed length control field | 02267nam a2200205Ia 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250408173457.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230421s2020||||xx |||||||||||||| ||eng|| |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780198828044 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 631 T73 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Trappenberg, P. Thomas |
| Relator term | Author |
| 245 #0 - TITLE STATEMENT | |
| Title | Fundamentals of machine learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | United Kingdom: |
| Name of publisher | Oxford University Press, |
| Year of publication | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | vii, 260p.; 21cms. |
| 500 ## - GENERAL NOTE | |
| General note | <br/>Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning. Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Fundamentals of Machine Learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Computer science and computational neuroscience |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Introduction to Bayesian approaches to modeling |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| -- | P29.99 |
| Withdrawn status | Lost status | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Full call number | Accession Number | Koha item type |
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| Dr. S. R. Ranganathan Library | Dr. S. R. Ranganathan Library | General Stacks | 006.31 T73 | 2788 | Books |