MIT 6.041: Probabilistic Systems Analysis
MIT's rigorous introduction to probability and its applications.
23h 23m
On-demand video
60
Lessons
Included
Certificate
Mobile & TV
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This course includes:
- 23h 23m on-demand video (free)
- 60 lessons
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
What you'll learn
- Model uncertainty with probability spaces and random variables
- Work with distributions, expectation, and variance
- Apply Bayes' rule, limit theorems, and inference
- Analyze random processes and Markov chains
Official study guide
The complete 61-page companion book for MIT 6.041: Probabilistic Systems Analysis, covering every module with worked examples, key terms, self-check questions, and a full glossary. Print it or read it offline.
Course content
8 sections · 60 lessons · 23h 23m- 1. Probability Models and AxiomsPreview51m
- The Probability of the Difference of Two Events5m
- Geniuses and Chocolates8m
- Uniform Probabilities on a Square9m
- 2. Conditioning and Bayes' Rule51m
- A Coin Tossing Puzzle8m
- Conditional Probability Example14m
- The Monty Hall Problem15m
Requirements
- •Single-variable calculus
About this course
MIT's rigorous introduction to probability and its applications. This curated learning path is organized into 8 modules and 60 lessons, taught by MIT OpenCourseWare. MegaCourse adds structure, learning outcomes, progress tracking, and a shareable certificate of completion.
Who this course is for
- STEM students
- Data scientists strengthening theory
Instructor
Trusted creator on MegaCourse
Licensing & attribution
License: CC BY-NC-SA 4.0
Source: MIT OpenCourseWare 6.041 (John Tsitsiklis) - CC BY-NC-SA 4.0
Videos are embedded from their original creators and remain free to watch. MegaCourse's paid layer covers our own study guides, graded exams, certificates, and curation.
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