UCB CS126 : Probability theory
- Offered by: UC Berkeley
- Prerequisites: CS70, Calculus, Linear Algebra
- Programming Languages: Python
- Difficulty: 🌟🌟🌟🌟🌟
- Class Hour: 100 hours
This is Berkeley's advanced probability course, which involves relatively advanced theoretical content such as statistics and stochastic processes, so a solid mathematical foundation is required. But as long as you stick with it you will certainly take your mastery of probability theory to a new level.
The course is designed by Professor Jean Walrand, who has written an accompanying textbook, Probability in Electrical Engineering and Computer Science, in which each chapter uses a specific algorithm as a practical example to demonstrate the application of theory in practice. Such as PageRank, Route Planing, Speech Recognition, etc. The book is open source and can be downloaded as a free PDF or Epub version.
Jean Walrand has also created accompanying Python implementations of the examples throughout the book, which are published online as Jupyter Notebook that readers can modify, debug and run them online interactively.
In addition to the Homework, nine Labs will allow you to use probability theory to solve practical problems in Python.
- Course Website: https://inst.eecs.berkeley.edu/~ee126/fa20/content.html
- Textbook: PDF, Epub, Jupyter Notebook
- Assignments: refer to the course website.
All the resources and assignments used by @PKUFlyingPig in this course are maintained in PKUFlyingPig/EECS126 - GitHub