000 | 03870nam a22005175i 4500 | ||
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001 | 978-3-030-18114-7 | ||
003 | DE-He213 | ||
005 | 20240423125303.0 | ||
007 | cr nn 008mamaa | ||
008 | 190712s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030181147 _9978-3-030-18114-7 |
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024 | 7 |
_a10.1007/978-3-030-18114-7 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
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_a006.3 _223 |
100 | 1 |
_aForsyth, David. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aApplied Machine Learning _h[electronic resource] / _cby David Forsyth. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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300 |
_aXXI, 494 p. 159 illus., 86 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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505 | 0 | _a1. Learning to Classify -- 2. SVM’s and Random Forests -- 3. A Little Learning Theory -- 4. High-dimensional Data -- 5. Principal Component Analysis -- 6. Low Rank Approximations -- 7. Canonical Correlation Analysis -- 8. Clustering -- 9. Clustering using Probability Models -- 10. Regression -- 11. Regression: Choosing and Managing Models -- 12. Boosting -- 13. Hidden Markov Models -- 14. Learning Sequence Models Discriminatively -- 15. Mean Field Inference -- 16. Simple Neural Networks -- 17. Simple Image Classifiers -- 18. Classifying Images and Detecting Objects -- 19. Small Codes for Big Signals -- Index. | |
520 | _aMachine 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. 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). Emphasizing theusefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that it’s worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 |
_aComputer science _xMathematics. |
|
650 | 0 | _aMathematical statistics. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aProbability and Statistics in Computer Science. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030181130 |
776 | 0 | 8 |
_iPrinted edition: _z9783030181154 |
776 | 0 | 8 |
_iPrinted edition: _z9783030181161 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-18114-7 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c176293 _d176293 |