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015 _aGBC466141
020 _a3031532813
020 _a9783031532818 (rel)
020 _a9783031532849 (brochØ)
_c53,10 EUR
020 _z9783031532825
020 _z3031532821
024 3 _a9783031532849
024 3 0 _a9783031532818
035 _a(OCoLC)1473700161
035 _aon1416891548
035 _aUKMGB300066141
040 _aABES
_bfre
_eAFNOR
041 0 _aeng
_2639-2
050 4 _aQA273
_b.A4146 2024
080 _a004
082 0 _a006.31
_223
100 1 _aAggarwal, Charu C.
245 1 0 _aProbability and statistics for machine learning :
_ba textbook /
_cCharu C. Aggarwal.
264 1 _aCham, Switzerland :
_bSpringer.
_c©2024
300 _axvii, 522 p. :
_bill. (some color), color portrait ;
_c26 cm.
336 _btxt
_2rdacontent
337 _bn
_2rdamedia
337 _bn
_2isbdmedia
338 _bnga
_2RDAfrCarrier
504 _aBibliographie (pages 515-517). Index. Exercices.
505 0 _a1. Probability and Statistics: An Introduction -- 2. Summarizing and Visualizing Data -- 3. Probability Basics and Random Variables -- 4. Probability Distributions -- 5. Hypothesis Testing and Confidence Intervals -- 6. Reconstructing Probability Distributions from Data -- 7. Regression -- 8. Classification: A Probabilistic View -- 9. Unsupervised Learning: A Probabilistic View -- 10. Discrete State Markov Processes -- 11. Probabilistic Inequalities and Extreme Value Analysis -- References -- Index
520 _a"This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5. 2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters. 3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations. The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners."--Back cover
650 7 _aApprentissage automatique.
_2ram
_9285793
650 7 _aProbabilitØs.
_2ram
650 7 _aStatistique.
_2ram
650 0 _aProbabilities.
_2lc
650 0 _aStatistics.
_2lc
650 0 _aMachine learning.
_2lc
_95593
650 0 _aMachine learning
_xStatistical methods.
_2lc
650 0 _aProbabilitØs.
_2lc
650 0 _aStatistique.
_2lc
650 0 _aApprentissage automatique.
_2lc
_9285793
650 0 _aApprentissage automatique
_xMØthodes statistiques.
_2lc
653 _aსაინფორმაციო ტექნოლოგიები
653 _aკომპიუტერული ტექნოლოგიები
653 _aკომპიუტერები
653 _aხელოვნური ინტელექტი
653 _aმანქანური სწავლება
653 _aმანქანური სწავლება, სტატისტიკური მეთოდები
653 _aმათემატიკა-ალბათობა და სტატისტიკა
653 _aალბათობა
653 _a სტატისტიკა
776 0 _tProbability and Statistics for Machine Learning : A Textbook / by Charu C. Aggarwal
_w(ABES)278791166
886 2 _2unimarc
_a181
_ai#
_bxxxe##
930 _a3.65 AGG
942 _2udc
_cBK
990 _aE
999 _c918084
_d918082