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Author:   Changho Suh
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Nature
Edition:   2024 ed.
ISBN:  

9789819740314

ISBN 10:   9819740312
Pages:   353
Publication Date:   09 January 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

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.

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Author Information

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.

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