stages:
- id: stage-1
  title: Imported from MARKDOWN
  concepts:
  - mit-ocw-6-050j-information-and-entropy-course-home
  - information-and-entropy
  - ultimate-limits-to-communication-and-computation
  - open-textbooks-problem-sets-and-programming-work
  - mit-ocw-6-050j-information-and-entropy-syllabus
  - prerequisites-and-mathematical-background
  - assessment-structure
  - course-notes-and-reference-texts
  - independent-reasoning-and-careful-comparison
  - mit-ocw-6-050j-information-and-entropy-unit-sequence
  - counting-and-probability
  - shannon-entropy
  - mutual-information
  - source-coding-and-compression
  - huffman-coding
  - channel-capacity
  - channel-coding
  - error-correcting-codes
  - cryptography-and-information-hiding
  - thermodynamics-and-entropy
  - reversible-computation-and-quantum-computation
  - course-synthesis
  checkpoint:
  - Summarize the course in one paragraph for a prospective learner.
  - List the main topic clusters that connect communication, computation, and entropy.
  - Explain how these resource types support both conceptual study and practice.
  - Decide whether a learner needs review in probability, linear algebra, or signals
    before beginning.
  - Build a study schedule that alternates reading, derivation, and worked exercises.
  - Compare when to use course notes versus outside references for clarification.
  - Write a short note distinguishing Shannon entropy, channel capacity, and thermodynamic
    entropy.
  - Derive a simple counting argument for binary strings and compute an event probability.
  - Compute the entropy of a Bernoulli source and interpret the result.
  - Compare independent variables with dependent variables using mutual-information
    reasoning.
  - Describe when compression succeeds and when it fails on already-random data.
  - Build a Huffman code for a small source alphabet.
  - State why reliable transmission above capacity is impossible in the long run.
  - Contrast uncoded transmission with coded transmission on a noisy channel.
  - Describe a simple parity-style code and its limits.
  - Compare a secure scheme with a weak one in terms of revealed information.
  - Compare the two entropy notions and identify what is preserved across the analogy.
  - Summarize how reversible computation changes the discussion of dissipation and
    information loss.
  - Produce a final study guide that links source coding, channel coding, secrecy,
    thermodynamic analogies, and computation.
