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
Ebook free download german Probability and Statistics for Machine Learning: A Textbook MOBI English version 9783031532818
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.
Probability for Statistics and Machine Learning
ISBN-13: 9781441996336, 978-1441996336. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. Probability for
Recommended texts
textbook for Stat Comprehensive but superficial coverage of all modern machine learning techniques for handling data. This is the standard text for learning
Probability for statistics and machine learning : fundamentals
This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical
Machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from
The Ultimate Guide to Statistics for Machine Learning
Apr 11, 2024 —
How Machine Learning Works
Machine learning is the general term for a collection of data A review of probability and statistics; Similarity Excellent book for someone wanting to know
Statistics and Probability
Learn statistics and probability—everything you'd want to know about descriptive and inferential statistics.
The Foundations of Machine Learning [Video]
What do you get with a Packt Subscription? This book & 7000+ ebooks & video courses on 1000+ technologies. 60+ curated reading lists for various learning
Statistics for Machine Learning
Statistics for Machine Learning. By Pratap By the end of the book, you will have mastered the statistics P-value: The probability of obtaining a test
An Introduction to Statistical Machine Learning
Discover the powerful fusion of statistics and machine learning. Explore how statistical techniques underpin machine learning models, enabling data-driven
Probability and Statistics for Deep Learning
Probability and Statistics for Deep Learning. Deep Learning is often called “Statistical Learning” and approached by many experts as statistical theory of