Changho Suh

Probability for Information Technology (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book introduces probabilistic modelling and explores its role in solving a broad spectrum of engineering problems that arise in Information Technology (IT). Divided into three parts, it begins by laying the foundation of basic probability concepts such as sample space, events, conditional probability, independence, total probability law and random variables. The second part delves into more advanced topics including random processes and key principles like Maximum A Posteriori (MAP) estimation, the law of large numbers and the central limit theorem. The last part applies these principles to various IT domains like communication, social networks, speech recognition, and machine learning, emphasizing the practical aspect of probability through real-world examples, case studies, and Python coding exercises.

A notable feature of this book is its narrative style, seamlessly weaving together probability theories with both classical and contemporary IT applications. Each concept is reinforced with tightly-coupled exercise sets, and the associated fundamentals are explored mostly from first principles. Furthermore, it includes programming implementations of illustrative examples and algorithms, complemented by a brief Python tutorial.

Departing from traditional organization, the book adopts a lecture-notes format, presenting interconnected themes and storylines. Primarily tailored for sophomore-level undergraduates, it also suits junior and senior-level courses. While readers benefit from mathematical maturity and programming exposure, supplementary materials and exercise problems aid understanding. Part III serves to inspire and provide insights for students and professionals alike, underscoring the pragmatic relevance of probabilistic concepts in IT.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 353
• Erscheinungstermin: 17. November 2024
• Englisch
• ISBN-13: 9789819740321
• Artikelnr.: 72303142

Autorenporträt

Changho Suh is a Professor of Electrical Engineering at KAIST. He received the B.S. and M.S. degrees in Electrical Engineering from KAIST in 2000 and 2002 respectively, and the Ph.D. degree in EECS from UC Berkeley in 2011. From 2011 to 2012, he was a postdoctoral associate in MIT. From 2002 to 2006, he was with Samsung. Prof. Suh is a recipient of numerous awards, including the 2022 Google Research Award, the 2021 James L. Massey Research & Teaching Award for Young Scholars from the IEEE Information Theory Society, the 2020 LINKGENESIS Best Teacher, the 2019 Google Education Grant, the 2018 IEIE/IEEE Joint Award, the 2015 IEIE Haedong Young Engineer Award, the 2013 IEEE Communications Society Stephen O. Rice Prize, the 2011 David J. Sakrison Memorial Prize (the best dissertation award in UC Berkeley EECS), the 2009 IEEE ISIT Best Student Paper Award, and the five Department Teaching Awards. Dr. Suh is a Fellow of the IEEE, a Treasurer of the IEEE Information Theory Society Board of Governors, and a TPC Co-Chair of the 2028 IEEE International Symposium on Information Theory. He served as an IEEE Information Theory Society Distinguished Lecturer, the General Chair of the Inaugural IEEE East Asian School of Information Theory, and a Member of Young Korean Academy of Science and Technology. He was also an Associate Editor of Machine Learning for the IEEE Transactions on Information Theory, the Editor for IEEE Information Theory Newsletter, a Column Editor for IEEE BITS the Information Theory Magazine, an Area Chair of NeurIPS 2021-2022 and a Senior Program Committee of IJCAI 2019-2021.

Inhaltsangabe

Preface.- Acknowledgements.- Part I. Basic concepts of probability.- Chapter 1. Overview of the book.- Chapter 2. Sample space and events.- Chapter 3. Monty Hall problem and Python implementation.- Problem Set 1.- Chapter 4. Conditional probability and total probability law.- Chapter 5. Independence.- Chapter 6. Coupon collector problem and Python implementation.- Problem Set 2.- Chapter 7. Random variables.- Chapter 8. Expectation.- Chapter 9. BitTorrent and Python implementation.- Chapter 10.Variance and Chebyshev's inequality.- Problem Set 3.- Chapter 11.Continuous random variables.- Chapter 12. Gaussian random variables.- Problem Set 4.- Part II. Introductory random processes and key principles.- Chapter 13. Introduction to random processes.- Chapter 14. Maximum A Posteriori (MAP) principle.- Chapter 15. MAP: Multiple observations.- Chapter 16. MAP: Performance analysis.- Chapter 17. MAP: Cancer prediciton and Python implementation.- Problem Set 5.- Chapter 18. Maximum Likelihood Estimation (MLE).- Chapter 19. MLE: Law of large numbers.- Chapter 20. MLE: Gaussian distribution.- Chapter 21. MLE: Gaussian distribution estimation and Python implementation.- Chapter 22. Central limit theorem.- Problem Set 6.- Part III. Information Technology Applications.- Chapter 23. Communication: Probabilistic modeling.- Chapter 24. Communication: MAP principle.- Chapter 25. Communication: MAP under multiple observations.- Chapter 26. Communication: Repetition coding and Python implementation.- Problem Set 7.- Chapter 27. Social networks: Probabilistic modeling.- Chapter 28. Social networks: ML principle.- Chapter 29. Social networks: Community detecition and Python implementation.- Problem Set 8.- Chapter 30. Speech recognition: Probabilistic modeling.- Chapter 31. Speech recognition: MAP principle.- Chapter 32. Speech recognition: Viterbi algorithm.- Chapter 33. Speech recognition: Python implementation.- Problem Set 9.- Appendix A: Python basics.- Bibliography.- Index.