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Science, Nature & Maths      Mathematics

Probability and Statistics for Machine Learning: A Textbook

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Book Details
Language
English
Publishers
Springer; 2024th edition (15 May 2024)
Weight
1.14 KG
Publication Date
21/06/2024
ISBN-10
3031532813
Pages
540 pages
ISBN-13
9783031532818
Dimensions
17.78 x 3.18 x 25.4 cm
SKU
9783031532818
Author Name
Charu C. Aggarwal (Author)
Charu Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his B.Tech. from IIT Kanpur in 1993 and his Ph.D. from Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining, with particular interests in data streams, privacy, uncertain data and social network analysis. He has published 19 (8 authored and 11 edited) books, over 400 papers in refereed venues, and has applied for or been granted over 80 patents. His h-index is 103. Because of the commercial value of the above-mentioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of an IBM Research Division Award (2008) for his scientific contributions to data stream research. He has received two best paper awards and an EDBT Test-of-Time Award (2014). He has served as the general or program co-chair of the IEEE Big Data Conference (2014), the ICDM Conference (2015), the ACM CIKM Conference (2015), and the KDD Conference (2016). He also co-chaired the data mining track at the WWW Conference 2009. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an action editor of the Data Mining and Knowledge Discovery Journal , an associate editor of the IEEE Transactions on Big Data, and an associate editor of the Knowledge and Information Systems Journal. He is an editor-in-chief of the ACM Transactions on Knowledge Discovery from Data. He received the IEEE ICDM Research Contributions award in 2015, and the ACM SIGKDD Innovations Award in 2019, which are the two highest awards in the field of data mining. He is also a recipient of the W. Wallace McDowell award, which is the highest award given solely by the IEEE Computer Society across the field of computer science. He received the distinguished alumnus award from IIT Kanpur in 2023. He is a fellow of the SIAM (2015), ACM (2013) and the IEEE (2010) for "contributions to knowledge discovery and data mining techniques."Read more about this authorRead less about this author
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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.

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