information Theory Course
This course equips you with essential information theory skills for real-world applications. Learn to model data sources like English logs, compute entropy and compression efficiency, design optimal Huffman codes, evaluate noisy channel performance including bit error rates, and optimise systems for reliable data transmission and storage in technology-driven environments.

flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain practical expertise in information theory by mastering entropy calculations, fixed-length and prefix-free coding techniques, and Huffman coding designs. Model sources with 10 symbols, assess compression ratios, encode actual messages, and analyse noisy channels for error detection, correction, and trade-offs to build efficient data systems.
Elevify advantages
Develop skills
- Design entropy-based codes by computing H, bits per symbol, and inefficiency swiftly.
- Build Huffman-style prefix codes to assign optimal bitstrings to symbols efficiently.
- Model text sources and estimate symbol probabilities from real English language data.
- Analyse noisy channels to quantify bit errors, propagation effects, and bit error rates.
- Balance compression efficiency with reliability by selecting block sizes, redundancy levels, and validation tests.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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