Probability and Statistics for Machine Learning : A Textbook / by Charu C. Aggarwal.

Format:

Book

Author/Creator:

Aggarwal, Charu C.

Language:

English

Subjects (All):

Machine learning.

Machine Learning.

Local Subjects:

Machine Learning.

Physical Description:

1 online resource (530 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

Chapter. 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.

ISBN:

3-031-53282-1