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Probability and Statistics for Machine Learning

Name: Probability and Statistics for Machine Learning
Brand: Springer, Berlin
SKU: 9783031532818
Price: 415.65 PLN
Availability: InStock

A Textbook

Autor: Charu C. Aggarwal

Język:

Oprawa: Twarda

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy

Wysyłamy za 10-13 dni

415.65 zł

This book covers probability and statistics from the machine learning perspective. The chapters of t...

Informacje o książce

Autor

Charu C. Aggarwal

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2024

strony

513

EAN

9783031532818

Enbook ID

44623809

Wydawca

Springer, Berlin

Waga

1130

Wymiary

178 x 254

Pełny opis

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 and 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 datadriven manner. Chapter 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.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 extended to more complex settings such as graphical data. Chapter 11 covers a number of useful concepts in extreme-value analysis.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.