Applied machine learning (Record no. 560789)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02470 a2200205 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190930154703.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190930b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783030181130 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIT Kanpur |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | F775a |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Forsyth, David |
| 245 ## - TITLE STATEMENT | |
| Title | Applied machine learning |
| Statement of responsibility, etc | David Forsyth |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Switzerland |
| Name of publisher | Springer |
| Year of publication | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxi, 494p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code.<br/><br/>A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).<br/><br/>Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:<br/>• classification using standard machinery (naive bayes; nearest neighbor; SVM)<br/>• clustering and vector quantization (largely as in PSCS)<br/>• PCA (largely as in PSCS)<br/>• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)<br/>• linear regression (largely as in PSCS)<br/>• generalized linear models including logistic regression<br/>• model selection with Lasso, elasticnet<br/>• robustness and m-estimators<br/>• Markov chains and HMM’s (largely as in PSCS)<br/>• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy<br/>• simple graphical models (in the variational inference section)<br/>• classification with neural networks, with a particular emphasis on<br/>image classification<br/>• autoencoding with neural networks<br/>• structure learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Mechanical engineering. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| General Stacks | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 14/10/2019 | 2 | 6228.18 | 006.31 F775a | A184835 | 7785.22 | Books |