Information Theory¶

Course Overview¶

• Institution: Stanford University
• Course code: EE 276
• Track: Information Theory and Coding
• Tier: A
• Role: Mainline
• Level: Not standardized by provider (use prerequisites)
• Last reviewed: 2026-07-28

Stanford University's Information Theory provides a current information-theory spine through slides and solved homework, with strong textual feedback but lecture video restricted to Canvas.

Why choose this course

Mainline course. A reliable option that can serve as a main course or strong alternative.

Before you start

• Recommended foundation: Probability, Statistics, and Random Processes
• Recommended foundation: Communication Systems

Verifiable learning outcomes

• Explain the core models in Information Theory and Coding, including their assumptions and limits
• Solve representative derivations and problems, checking units, limiting cases, or numerical results

Workload and pacing

11 weeks at 9 hours/week. This maintainer planning estimate is derived from course role and the density of public practice and labs; it is not a provider workload promise. Pilot two weeks while logging instruction, practice, lab, and review time, then adjust the remaining plan when actual effort differs by more than 25%.

Safety level

Simulation only. The default practice scope is software, computation, or simulation only; a lab label in the resource inventory does not authorize connecting physical equipment, and any hardware extension requires provider-scope verification and a new risk assessment.

Course Resources¶

Software, hardware, and cost

Software

• Maintainer-suggested open-source/free verification path: Python 3, Jupyter, NumPy, SciPy, and SageMath
• The resource inventory does not list public code coverage; the tools above are only a maintainer-suggested independent check, not a provider requirement

Hardware

• The resource inventory does not list public physical-lab coverage; the maintainer path defaults to computation/simulation. It assumes only a general-purpose computer that can rerun coding experiments and retain seeds and results; no dedicated physical hardware is assumed. If the provider lists different equipment or compute requirements, follow its course page

Cost note

The suggested software stack is available open source or free; this is maintainer planning, not a provider requirement. If the provider specifies commercial licenses, cloud compute, storage, or institutional resources, costs vary by plan, region, and institution, so no fixed price is asserted here.

Public resource coverage

Resources and access

“Listed by official page” means the link was discovered on a successfully fetched official source on the verification date; it does not guarantee that every region or account can open the target directly. Access does not grant redistribution rights. Re-check the provider page, target link, and third-party notices before downloading, adapting, or publishing material.

Practice and Verification¶

Practice loop

Information Theory · Stanford University EE 276: Source/Channel Coding Boundary Experiment

This is a maintainer-suggested self-study project for Information Theory · Stanford University EE 276, not an official course assignment. Implement a compression or error-correcting code for Information Theory and Coding and compare entropy or capacity bounds with finite-blocklength rate, distortion, and error.

Origin: Maintainer-suggested project

Deliverables

• A derivation of source or channel model, code construction, block length, rate, and theoretical bound
• Encoder, decoder, random baseline, and unit-test sources
• Raw code length, distortion, error, and runtime across at least five block lengths or noise points
• A report comparing theory and implementation and analyzing short blocks, burst errors, or model mismatch

Verification

• Round-trip at least 10,000 random messages bit-exactly in the noiseless case
• Keep measured average rate above the entropy bound and show the gap shrinking with block length as expected
• Cross-check decoding optimality by exhaustive small-block search or a second implementation
• Inject burst errors or a mismatched prior and report degradation relative to the IID baseline

Reproducibility

• Commit derivation, codec, baseline, test, and plotting sources
• Pin block lengths, codebook generation, seeds, stopping rules, and dependencies
• Preserve per-block raw results, failed messages, and the generated report

Safety boundary: Simulation only — Use synthetic bitstreams only; do not deploy the unvalidated codec to emergency communications, medical telemetry, or safety-critical storage.

Risks, gaps, and boundaries

Current slides and homework solutions are public, but lecture video is restricted to Canvas.

Completion evidence

• Weekly learning log with time, questions, corrected errors, decisions, next steps, and links to that week's reproducible artifacts
• Theory dossier with explicit assumptions, notation, derivation, units, and boundary conditions, checked by at least one independent method
• Code repository with pinned dependencies and toolchain, a minimal run command, tests or waveform/benchmark checks, expected output, and license notes