LEADER 01205nam0-22003371i-450- 001 990008108140403321 005 20050509154139.0 035 $a000810814 035 $aFED01000810814 035 $a(Aleph)000810814FED01 035 $a000810814 100 $a20050509d1994----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $a--------001yy 200 1 $aBeatrice nell'opera di Dante e nella memoria europea, 1290-1990$eatti del Convegno Internazionale, 10-14 dicembre 1990$fa cura di Maria Picchio Simonelli$gcon la collaborazione di Amalia Cecere e Mariarosaria Spinetti 210 $aNapoli$cCadmo$dc1994 215 $a543 p.$d24 cm 300 $aSul front.: Istituto Universitario Orientale Napoli, Dipartimento di studi letterari e linguistici dell'Occidente 610 0 $aAlighieri, Dante 676 $a851.1 702 1$aPicchio Simonelli,$bMaria 702 1$aCecere,$bAmalia 702 1$aSpinetti,$bMariarosaria 801 0$aIT$bUNINA$c20050509$gRICA$2UNIMARC 901 $aBK 912 $a990008108140403321 952 $aF.Russo Dante/s 202$fBAT 959 $aBAT 996 $aBeatrice nell'opera di Dante e nella memoria europea, 1290-1990$9256230 997 $aUNINA LEADER 03694nam 22004455 450 001 9910861088903321 005 20250807153053.0 010 $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 $aMachine Learning 615 0$aMachine learning. 615 14$aMachine Learning. 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