Probability and statistics for machine learning: a textbook

By:

Aggarwal, Charu C

Material type: Text

TextPublication details: Springer Cham 2024Description: xvii, 522 pISBN:

9783031532818

Subject(s):

DDC classification:

519.2 AGG

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. (https://link.springer.com/book/10.1007/978-3-031-53282-5)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.2 AGG (Browse shelf(Opens below))	1	Available		007880

Table of contents:
Front Matter
Pages i-xviii
Download chapter PDF
Probability and Statistics: An Introduction
Charu C. Aggarwal
Pages 1-23
Summarizing and Visualizing Data
Charu C. Aggarwal
Pages 25-64
Probability Basics and Random Variables
Charu C. Aggarwal
Pages 65-126
Probability Distributions
Charu C. Aggarwal
Pages 127-190
Hypothesis Testing and Confidence Intervals
Charu C. Aggarwal
Pages 191-243
Reconstructing Probability Distributions from Data
Charu C. Aggarwal
Pages 245-301
Regression
Charu C. Aggarwal
Pages 303-351
Classification: A Probabilistic View
Charu C. Aggarwal
Pages 353-391
Unsupervised Learning: A Probabilistic View
Charu C. Aggarwal
Pages 393-433
Discrete State Markov Processes
Charu C. Aggarwal
Pages 435-484
Probabilistic Inequalities and Approximations
Charu C. Aggarwal
Pages 485-514
Back Matter
Pages 515-522

(https://link.springer.com/book/10.1007/978-3-031-53282-5)

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.

(https://link.springer.com/book/10.1007/978-3-031-53282-5)

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