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001 978-3-030-18114-7
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020 _a9783030181147
_9978-3-030-18114-7
024 7 _a10.1007/978-3-030-18114-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aForsyth, David.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXXI, 494 p. 159 illus., 86 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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