Probability and Statistics for Machine Learning : A Textbook Hardback - 2024
by Charu C. Aggarwal
- New
- Hardback
A$176.06
A$15.28
Delivery to USA
Standard delivery: 7 to 12 days
More delivery options
Standard delivery: 7 to 12 days
Ships from Ria Christie Collections (Greater London, United Kingdom)
Details
- Title Probability and Statistics for Machine Learning : A Textbook
- Author Charu C. Aggarwal
- Binding Hardback
- Condition New
- Pages 522
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2024-05-15
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # ria9783031532818_inp
- ISBN 9783031532818 / 3031532813
- Weight 2.53 lbs (1.15 kg)
- Dimensions 10 x 7 x 1.19 in (25.40 x 17.78 x 3.02 cm)
- Category Computers - General Information
- Quantity available 866
About Ria Christie Collections Greater London, United Kingdom
Biblio member since 2014
Hello We are professional online booksellers. We sell mostly new books and textbooks and we do our best to provide a competitive price. We are based in Greater London, UK. We pride ourselves by providing a good customer service throughout, shipping the items quickly and replying to customer queries promptly. Ria Christie Collections
30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.
Reader reviews for Probability and Statistics for Machine Learning : A Textbook
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the publisher
From the rear cover
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.
