Probability and Statistics for Machine Learning: A Textbook by Charu C. Aggarwal on Ipad
Probability and Statistics for Machine Learning: A Textbook. Charu C. Aggarwal
Probability-and-Statistics-for.pdf
ISBN: 9783031532818 | 522 pages | 14 Mb
- Probability and Statistics for Machine Learning: A Textbook
- Charu C. Aggarwal
- Page: 522
- Format: pdf, ePub, fb2, mobi
- ISBN: 9783031532818
- Publisher: Springer Nature Switzerland
Easy ebook downloads Probability and Statistics for Machine Learning: A Textbook 9783031532818 (English Edition) by Charu C. Aggarwal PDB DJVU
Overview
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.
Download more ebooks:
Read online: Printemps silencieux
[Pdf/ePub] Ne coupez jamais la poire en deux - Un manuel redoutable pour négocier gagnant par un négociateur du FBI by Chris Voss, Tahl Raz download ebook
ESO NO ESTABA EN MI LIBRO DE HISTORIA DE LOS VIDEOJUEGOS leer el libro
[Pdf/ePub] Planches énergétiques de santé et symboles de guérison - Nouvelle homéopathie, kinésiologie et radiesthésie. Cadrans diagnostiques pour thérapeutes ou en autotraitement by Christina Baumann, Roswitha Stark download ebook
Read online: Tout le FLE A1 A2 - 160 séquences et 400 exercices corrigés