Suggested preparation: Proof-writing, elementary probability and linear algebra.
About this course
Build a toolkit for discrete problem solving through counting, concentration inequalities, optimization and coding. MIT lectures show how relatively simple mathematical ideas support more demanding arguments.
Topics and lesson examples
- Lecture 16: Data Compression and Shannon’s Noiseless Coding Theorem
- Lecture 18: Transmitting Information Reliably over a Noisy Channel & Shannon’s Noisy Coding Theorem
- Lecture 19: Error-Correcting Codes—Hamming Codes
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Intermediate — WikiFree study-level assessment |
| Language | English |
| Available lessons | 20 |
| Video duration | 24h 42m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Proof-writing, elementary probability and linear algebra.
Follow the available videos in sequence, pausing to summarize the main argument or reconstruct a worked example. Keep a short list of assumptions, unfamiliar terms and questions to revisit. Additional reading, problem solving and practice will take time beyond the listed video duration.
Using this video classroom
Select a lesson from the list, or search for a topic. Previous and Next move through the sequence. You can mark lessons complete; progress is stored in this browser without a WikiFree account.
Free access and course scope
The public MIT OpenCourseWare videos in this classroom are free to watch. This listing provides the available playlist recordings, which may include selected lectures, recitations, demonstrations or supporting discussions rather than every component of the original campus course. Textbooks, equipment, software licenses, supervised labs, graded assignments and instructor feedback are not supplied by WikiFree. No purchase is required to watch the videos or use the note-based study approach above.
WikiFree independently organizes these resources for self-paced study. Watching does not award an MIT certificate, academic credit or admission to MIT.
Video provider and credit: MIT OpenCourseWare on YouTube. WikiFree titles, descriptions and cover artwork are independently prepared.
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Intermediate
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free MIT Course: Discrete Applied Mathematics | Intermediate | Lecture 16: Data Compression and Shannon’s Noiseless Coding Theorem · Lecture 18: Transmitting Information Reliably over a Noisy Channel & Shannon’s Noisy Coding Theorem · Lecture 19: Error-Correcting Codes—Hamming Codes | 24h 42m of video; practice time is additional |
| Free MIT Course: Discrete Mathematics for Computer Science | Intermediate | MIT Course: Discrete Mathematics for Computer Science | 31h 40m of video; practice time is additional |
| Free MIT Course: Mathematics Problem Solving for Entrance Exams | Intermediate | Video 1: Introduction to Vectors - Position, Line, Free and Unit Vectors · Video 3: The Binomial Theorem · Video 5: Introduction to Trigonometry | 2h 23m of video; practice time is additional |
| Integrated Mathematics 1: Free Course | Intermediate | Linear modeling · Functions and sequences · Coordinate geometry | 25h 46m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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