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Information and Coding TheoryWinter 2021Lecture 12: February 18, 2021, Lecturer: Mather TulsianiIn the previous lecture, we discussed how a random collection of code words can be used to achieve capacity
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01
Calculate the probability of each symbol occurring in the message.
02
Calculate the information content of each symbol using the formula -log2(p), where p is the probability of the symbol.
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Multiply the information content of each symbol by its probability to get the entropy of that symbol.
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Sum up the entropy of all symbols to get the total information entropy of the message.
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Use coding techniques such as Huffman coding to efficiently encode the message based on the calculated information entropy.

Who needs information entropy and coding?

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Information entropy and coding are used in various fields such as data compression, cryptography, and information theory.
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Programmers and data scientists dealing with large amounts of data can benefit from understanding and using information entropy and coding techniques.
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Individuals working in cybersecurity, data analysis, and communication systems may also need to understand and implement information entropy and coding for secure and efficient data transmission.
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Information entropy is a measure of uncertainty associated with a random variable, while coding is the process of converting information into a format that can be transmitted and stored efficiently.
Individuals or organizations collecting, storing, or transmitting data may be required to file information entropy and coding.
Information entropy and coding forms can typically be filled out electronically through a designated platform or software.
The purpose of information entropy and coding is to ensure that data is efficiently and accurately transmitted and stored.
Information entropy and coding forms may require details about the type of data being collected, stored, or transmitted, as well as details about the methods used for encoding.
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