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Machine learning : the art and science of algorithms that make sense of data

Machine learning : the art and science of algorithms that make sense of data (Loan 11 times)

Material type
단행본
Personal Author
Flach, Peter A.
Title Statement
Machine learning : the art and science of algorithms that make sense of data / Peter Flach.
Publication, Distribution, etc
Cambridge ;   New York :   Cambridge University Press,   2012.  
Physical Medium
xvii, 396 p. : col. ill. ; 25 cm.
ISBN
9781107096394 (hbk.) 1107096391 (hbk.) 9781107422223 (pbk.) 1107422221 (pbk.)
요약
'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.
Content Notes
1. The ingredients of machine learning -- 2. Binary classification and related tasks -- 3. Beyond binary classification -- 4. Concept learning -- 5. Tree models -- 6. Rule models -- 7. Linear models -- 8. Distance-based models -- 9. Probabilistic models -- 10. Features -- 11. Model ensembles -- 12. Machine learning experiments -- Epilogue: where to go from here.
Bibliography, Etc. Note
Includes bibliographical references (p. 367-381) and index.
Subject Added Entry-Topical Term
Machine learning --Textbooks.
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020 ▼a 1107422221 (pbk.)
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100 1 ▼a Flach, Peter A.
245 1 0 ▼a Machine learning : ▼b the art and science of algorithms that make sense of data / ▼c Peter Flach.
260 ▼a Cambridge ; ▼a New York : ▼b Cambridge University Press, ▼c 2012.
300 ▼a xvii, 396 p. : ▼b col. ill. ; ▼c 25 cm.
504 ▼a Includes bibliographical references (p. 367-381) and index.
505 0 0 ▼g 1. ▼t The ingredients of machine learning -- ▼g 2. ▼t Binary classification and related tasks -- ▼g 3. ▼t Beyond binary classification -- ▼g 4. ▼t Concept learning -- ▼g 5. ▼t Tree models -- ▼g 6. ▼t Rule models -- ▼g 7. ▼t Linear models -- ▼g 8. ▼t Distance-based models -- ▼g 9. ▼t Probabilistic models -- ▼g 10. ▼t Features -- ▼g 11. ▼t Model ensembles -- ▼g 12. ▼t Machine learning experiments -- ▼g Epilogue: ▼t where to go from here.
520 3 ▼a 'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.
650 0 ▼a Machine learning ▼v Textbooks.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 F571m Accession No. 121234395 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Prologue: a machine learning sampler; 1. The ingredients of machine learning; 2. Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where to go from here; Important points to remember; Bibliography; Index.


Information Provided By: : Aladin

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