Logic and Information
by Keith Devlin
Publisher: ESSLLI 2001
An introductory, comparative account of three mathematical approaches to information: the classical quantitative theory of Claude Shannon, developed in the 1940s and 50s, a quantitative-based, qualitative theory developed by Fred Dretske in the 1970s, and a qualitative theory introduced by Jon Barwise and John Perry in the early 1980s and pursued by Barwise, Israel, Devlin, Seligman and others in the 1990s.
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by Neri Merhav - arXiv
Lecture notes for a graduate course focusing on the relations between Information Theory and Statistical Physics. The course is aimed at EE graduate students in the area of Communications and Information Theory, or graduate students in Physics.
by Martin Tomlinson, et al. - Springer
This book discusses both the theory and practical applications of self-correcting data, commonly known as error-correcting codes. The applications included demonstrate the importance of these codes in a wide range of everyday technologies.
by Peter D. Gruenwald, Paul M.B. Vitanyi - CWI
We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain this quantitative approach to defining information and discuss the extent to which Kolmogorov's and Shannon's theory have a common purpose.
by John Daugman - University of Cambridge
The aims of this course are to introduce the principles and applications of information theory. The course will study how information is measured in terms of probability and entropy, and the relationships among conditional and joint entropies; etc.