# MIT OCW 6.050J Information and Entropy: Syllabus

Source: https://ocw.mit.edu/courses/6-050j-information-and-entropy-spring-2008/pages/syllabus/
Attribution: adapted from the MIT OpenCourseWare syllabus page for 6.050J Information and Entropy.

## Course Logistics

### Prerequisites and Mathematical Background
- Objective: Explain the mathematical maturity expected by the course.
- Exercise: Decide whether a learner needs review in probability, linear algebra, or signals before beginning.
The syllabus expects a foundation comparable to MIT subjects in calculus and linear algebra, together with comfort in probability, signals, and basic programming. Didactopus should therefore surface prerequisite review when those foundations appear weak.

### Assessment Structure
- Objective: Identify the role of problem sets, exams, and programming work in the course.
- Exercise: Build a study schedule that alternates reading, derivation, and worked exercises.
The syllabus emphasizes regular problem solving and quantitative reasoning. The course is not only a reading list: learners are expected to derive results, solve structured problems, and connect abstract arguments to implementation-oriented tasks.

## Reading Base

### Course Notes and Reference Texts
- Objective: Explain how the course notes and textbook references supply the core conceptual sequence.
- Exercise: Compare when to use course notes versus outside references for clarification.
MIT OCW links course notes and textbook-style resources through the syllabus and resource pages. The intended use is cumulative: earlier notes establish counting, probability, and entropy, while later materials expand into coding, noise, secrecy, thermodynamics, and computation.

## Learning Norms

### Independent Reasoning and Careful Comparison
- Objective: Explain why the course requires precise comparison of related but non-identical concepts.
- Exercise: Write a short note distinguishing Shannon entropy, channel capacity, and thermodynamic entropy.
The syllabus framing implies a style of work where analogy is useful but dangerous when used loosely. Learners must compare models carefully, state assumptions, and notice where similar mathematics does not imply identical interpretation.
