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OverviewFull Product DetailsAuthor: Changho Suh (KAIST, South Korea)Publisher: now publishers Inc Imprint: now publishers Inc Weight: 0.774kg ISBN: 9781638281146ISBN 10: 1638281149 Pages: 250 Publication Date: 03 April 2023 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Language: English Table of Contents1 Source Coding 1.1 Overview of the book 1.2 Entropy and Python exercise 1.3 Mutual information, Kullback-Leibler (KL) divergence and Python exercise Problem Set 1 1.4 Source coding theorem for i.i.d. sources (1/3) 1.5 Source coding theorem for i.i.d. sources (2/3) 1.6 Source coding theorem for i.i.d. sources (3/3)Problem Set 2 1.7 Source code design 1.8 Source coding theorem for general sources 1.9 Huffman code and Python implementation Problem Set 3 2 Channel Coding 2.1 Statement of channel coding theorem 2.2 Achievability proof for the binary erasure channel 2.3 Achievability proof for the binary symmetric channelProblem Set 4 2.4 Achievability proof for discrete memoryless channels 2.5 Converse proof for discrete memoryless channels 2.6 Source-channel separation theorem and feedback Problem Set 5 2.7 Polar code: Polarization 2.8 Polar code: Implementation of polarization 2.9 Polar code: Proof of polarization and Python simulation Problem Set 6 3 Data Science Applications 3.1 Social networks: Fundamental limits 3.2 Social networks: Achievability proof 3.3 Social networks: Converse proof 3.4 Social networks: Algorithm and Python implementationProblem Set 7 3.5 DNA sequencing: Fundamental limits 3.6 DNA sequencing: Achievability proof 3.7 DNA sequencing: Converse proof 3.8 DNA sequencing: Algorithm and Python implementationProblem Set 8 3.9 Top-K ranking: Fundamental limits 3.10 Top-K ranking: Algorithm 3.11 Top-K ranking: Python implementation Problem Set 9 3.12 Supervised learning: Connection with information theory 3.13 Supervised learning: Logistic regression and cross entropy 3.14 Supervised learning: TensorFlow implementation Problem Set 10 3.15 Unsupervised learning: Generative modeling 3.16 Generative Adversarial Networks (GANs) and KL divergence 3.17 GANs: TensorFlow implementation Problem Set 11 3.18 Fair machine learning and mutual information (1/2) 3.19 Fair machine learning and mutual information (2/2) 3.20 Fair machine learning: TensorFlow implementation Problem Set 12 Appendices A – Python Basics B – TensorFlow and Keras Basics C – Note on ResearchReviewsBy going through the proposal and the credentials of the author, I can instantly tell that the proposed book will be looked forward to by the community. The book is going to build a bridge between information theory and data science (also involving some AI). It will be a very useful tool not only for final-year undergraduate and graduate students, but also for researchers (for example myself) who want to understand the relation between the two subjects but do not want to invest too much time on it. The author has a stellar track record. He has worked with and learned from very top people in related fields, and he has received many awards for his research work. In summary, I am confident that this proposed title will be a highly valuable addition to the literature. - Raymond W Yeung, The Chinese University of Hong Kong--Raymond W Yeung (9/24/2022 12:00:00 AM) By going through the proposal and the credentials of the author, I can instantly tell that the proposed book will be looked forward to by the community. The book is going to build a bridge between information theory and data science (also involving some AI). It will be a very useful tool not only for final-year undergraduate and graduate students, but also for researchers (for example myself) who want to understand the relation between the two subjects but do not want to invest too much time on it. The author has a stellar track record. He has worked with and learned from very top people in related fields, and he has received many awards for his research work. In summary, I am confident that this proposed title will be a highly valuable addition to the literature. - Raymond W Yeung, The Chinese University of Hong Kong -- Raymond W Yeung Author InformationChangho Suh is an Associate Professor of Electrical Engineering at KAIST and an Associate Head of KAIST AI Institute. 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 Electrical Engineering and Computer Sciences from UC Berkeley in 2011. From 2011 to 2012, he was a postdoctoral associate at the Research Laboratory of Electronics in MIT. From 2002 to 2006, he was with Samsung Electronics. Tab Content 6Author Website:Countries AvailableAll regions |
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