LEADER 01211nam2-22003731i-450- 001 990002003850403321 005 20090409114405.0 035 $a000200385 035 $aFED01000200385 035 $a(Aleph)000200385FED01 035 $a000200385 100 $a20030910g19031909km-y0itay50------ba 101 0 $aeng 102 $aGB 105 $aa-------001yy 200 1 $aIntroduction and protozoa$fS. J. Hickson, J. B. Farmer$gE. Ray Lankester 210 $aLondra$cAdam and Charles Black$d1903-1909 215 $a2 fascicoli$d23 cm 225 1 $a<>treatise on zoology$fedited by E. Ray Lankester$v1 327 1 $a1.: 296 p. / S. J. Hickson$a2.: 451 p. / J. B. Farmer 461 0$1001000200423$12001$a<>treatise on zoology 610 0 $aZoologia$aTrattati 676 $a593.1 700 1$aHickson,$bSydney John$f<1859-1940>$088466 701 1$aFarmer,$bJohn Bretland$0314469 702 1$aLankester,$bEdwin Ray$c 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002003850403321 952 $a61 II A.5/019$b129$fDAGEN 952 $a61 II A.5/019.1$b129$fDAGEN 959 $aDAGEN 996 $aIntroduction and protozoa$9403038 997 $aUNINA LEADER 03953nam 22005295 450 001 9910861088903321 005 20260323115143.0 010 $a3-031-53282-1 024 7 $a10.1007/978-3-031-53282-5 035 $a(CKB)32027758500041 035 $a(MiAaPQ)EBC31342491 035 $a(Au-PeEL)EBL31342491 035 $a(DE-He213)978-3-031-53282-5 035 $a(EXLCZ)9932027758500041 100 $a20240514d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProbability and Statistics for Machine Learning $eA Textbook /$fby Charu C. Aggarwal 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (530 pages) 311 08$a3-031-53281-3 327 $aChapter. 1. Probability and Statistics: An Introduction -- Chapter. 2. Summarizing and Visualizing Data -- Chapter. 3. Probability Basics and Random Variables -- Chapter. 4. Probability Distributions -- Chapter. 5. Hypothesis Testing and Confidence Intervals -- Chapter. 6. Reconstructing Probability Distributions from Data -- Chapter. 7. Regression -- Chapter. 8. Classification: A Probabilistic View -- Chapter. 9. Unsupervised Learning: A Probabilistic View -- Chapter. 10. Discrete State Markov Processes -- Chapter. 11. Probabilistic Inequalities and Extreme Value Analysis -- Bibliography -- Index. 330 $aThis 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. 606 $aMachine learning 606 $aMachine Learning 606 $aProbabilitats$2thub 606 $aEstadística$2thub 606 $aAprenentatge automàtic$2thub 608 $aLlibres electrònics$2thub 615 0$aMachine learning. 615 14$aMachine Learning. 615 7$aProbabilitats 615 7$aEstadística 615 7$aAprenentatge automàtic 676 $a006.31 700 $aAggarwal$b Charu C$0518673 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910861088903321 996 $aProbability and Statistics for Machine Learning$94163262 997 $aUNINA