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Information Theory and Coding

InformationTheoryandCodingComputerScienc eTripos PartII, MichaelmasTerm11 Lecturesby J G Daugman1. Foundations: probability , Uncertainty, andInformation2. EntropiesDe ned,andWhy theyareMeasuresof Information3. SourceCodingTheorem;Pre x,Variable-,& Fixed-LengthCodes4. ChannelTypes,Properties,Noise,andChannel Capacity5. ContinuousInformation;Density; NoisyChannelCodingTheorem6. FourierSeries,Convergence,OrthogonalRepr esentation7. UsefulFourierTheorems;TransformPairs;Sam pling;Aliasing8. TheQuantizedDegrees-of-Freedomin a \Logons" Complexity andMinimalDescriptionLengthInformationTh eoryandCodingJ G DaugmanPrerequisitecourses: probability ;M athematical Methods forCS;DiscreteMathematicsAimsTheaimsof thiscourseareto introducetheprinciplesandapplicationsof informationis measuredin termsof probability andentropy, andtherelatio

a known probability distribution for any given natural language. An analog speech signal represented by a voltage or sound pressure wave-form as a function of time (perhaps with added noise), is a continuous random variable having a continuous probability density function. Most of Information Theory involves probability distributions of ran-

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