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Lecture
Understanding Computer Information
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Related lectures (28)
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Data Compression and Shannon's Theorem: Huffman Codes
Explores the performance of Shannon-Fano algorithm and introduces Huffman codes for efficient data compression.
Conditional Entropy and Data Compression Techniques
Discusses conditional entropy and its role in data compression techniques.
Computing: Computer Components
Covers the basics of computing, human computers, Von Neumann architecture, and memory units.
Channel Coding: Convolutional Codes
Explores channel coding with a focus on convolutional codes, emphasizing error detection, correction, and decoding processes.
Source Coding and Prefix-Free Codes
Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.
Big Data Challenges: Distributed Computing with Spark
Explores big data challenges, distributed computing with Spark, RDDs, hardware requirements, MapReduce, transformations, and Spark DataFrames.
Anonymity Online: Techniques and Weaknesses
Explores online anonymity techniques and weaknesses, including bypassing geo-blocking and avoiding tracking.
Data Compression and Shannon's Theorem Summary
Summarizes Shannon's theorem, emphasizing the importance of entropy in 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.
Data Issues in Research
Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.
