Some homework problems will be assigned from the text.There will be weekly assigned problem sets, one take-home midterm, and one take-home final.
Information Theory, Inference and Learning Algorithms by David J.C. MacKay. Information Theory Spring 2020, Time: MWF 11:30AM – 12:20PM, Room LPH 103 Instructor Dr. Massimiliano Pierobon Assistant Professor 104 Schorr Center Department of Computer Science and Engineering University of Nebraska-Lincoln Lincoln, NE 68508 Tel: (402) 472-5021 Fax: (402) 472-7767 E-mail: pierobon@cse.unl.edu Office Hours TBD or by appointment.
The text book for the course will be Quantum Computation and Quantum Information by M. A. Nielsen and I. L. Chuang (Cambridge, 2000).. Additional References: Elements of Information Theory, by Thomas M. Cover and Joy A. Thomas, John Wiley, 1991, ISBN 0-471-06259-6.
Practical compression and error correction. The concepts of information theory extend far beyond communication theory, however, and have influenced diverse fields from physics to computer science to biology.
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This course covers multi-user information theory, information theory for Gaussian channels (including fading and MIMO), and information theory for ad-hoc and sensor networks.
This is a graduate-level introduction to mathematics of information theory. Those taking Information Theory for the first time may benefit from reading the standard textbook by T. Cover and J. Thomas (see below). You may collaborate on problem sets, but you must indicate for each problem the name(s) of your collaborator(s).There will be a project based on one of the extra papers. Text Book. 4 0 obj Syllabus Calendar Readings Lecture Notes Assignments ... 6.441 offers an introduction to the quantitative theory of information and its applications to reliable, efficient communication systems. %PDF-1.3
The notion of entropy, which is fundamental to the whole topic of this book, is introduced here. Indeed the diversity and directions of their perspectives and interests shaped the direction of Information Theory. We will cover both classical and modern topics (such as finite blocklength IT and applications to statistical decision theory). INTRODUCTION TO INFORMATION THEORY {ch:intro_info} This chapter introduces some of the basic concepts of information theory, as well as the definitions and notations of probabilities that will be used throughout the book. Concept of information, average information content, conditional entropy, mutual information, channel capacity : discrete memoryless channel, binary symmetric channel, continuous channel. With more than 2,400 courses available, OCW is delivering on the promise of open sharing of knowledge. Information Theory on Mac INFORMATION THEORY Syllabus: (10-13 hrs., 10-13 wks.)
The final grade will be weighted as follows:This is one of over 2,200 courses on OCW. Prerequisite: Undergraduate Semester level ECE 3040 Minimum Grade of D and (Undergraduate Semester level ECE 3770 Minimum Grade of D or Undergraduate Semester level ISYE 3770 Minimum Grade of D or Undergraduate Semester level … The project will require a project presentation of 12 minutes per person (the presentation time per project will be proportional to the number of people in the group). The choice of papers will be up to the group. Your use of the MIT OpenCourseWare site and materials is subject to our How information theory bears on the design and operation of modern-day systems such as smartphones and the Internet.
Offered by The Chinese University of Hong Kong. The channels studied are primarily motivated by wireless communication systems and ad-hoc & sensor networks. One star: Accessible. �U��e_)��������Y��n������ȇ''�V�������D� �jl�v���)�GD�{U�v\ �v�用��Ԡϒt����N>.ݟ��3�RU�U�9����W�ԃ��|"�Ś���jP�����tc5tחc��FA=bE95��j0���Q5)d��z�HZ�M�_u^t���\�' �z7lp���^����! Copies will be kept on reserve in the library.
Description This course deals with … Three stars: Expert level.This is one of over 2,200 courses on OCW. Those taking Information Theory for the first time may benefit from reading the standard textbook by T. Cover and J. Thomas (see below).This course requires knowledge of theorem-proof exposition and probability theory, as taught in In addition to the lecture notes, students may find the following text to be of use. Find materials for this course in the pages linked along the left.
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Information Theory: Introduction, Measure of information, Information content of message, Average Information content of symbols in Long Independent sequences, Average Information content of symbols in Long dependent sequences, Markov Statistical Model of Information Sources, Entropy and Information rate of Markoff Sources (Section 4.1, 4.2 of Text 1).
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