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Lecture
Information Theory: Source Coding, Cryptography, Channel Coding
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Related lectures (24)
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Lecture: Shannon
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Channel Coding: Convolutional Codes
Explores channel coding with a focus on convolutional codes, emphasizing error detection, correction, and decoding processes.
Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Conditional Entropy and Data Compression Techniques
Discusses conditional entropy and its role in data compression techniques.
Information Theory Basics
Introduces information theory basics, including entropy, independence, and binary entropy function.
Conditional Entropy and Information Theory Concepts
Discusses conditional entropy and its role in information theory and data compression.
Entropy and Data Compression: Huffman Coding Techniques
Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.
Source Coding Theorem
Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.
Channel Coding and BICM (LLRs)
Explores channel coding, BICM, and LLRs in wireless communication systems, emphasizing the importance of error detection and correction.
Data Compression and Shannon-Fano Algorithm
Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.
