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Lecture
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
This lecture by the instructor covers the Source Coding Theorem, which discusses the fundamental principles of compressing information. It delves into the concepts of entropy, Huffman coding, and the impact of conditioning on reducing entropy. The lecture explores the bounds and proofs related to entropy reduction through conditioning, emphasizing the importance of entropy as a measure of information. It also touches on the implications of infinite source alphabets and the encoding of positive integers using prefix-free codes. The lecture concludes with a discussion on the Source Coding Theorem's applications and outlook for universal source coding.