Probability

Here are some of my course notes as a former teaching assistant for EECS 126: Probability and Random Processes at UC Berkeley. (Asterisks indicate out-of-scope content.)

• Note 00. Symmetry
• Note 01. Probability spaces
• Note 02. Conditional probability and independence
• Note 03. Random variables
• Note 04. Discrete distributions
• Note 05. Continuous distributions
• Note 06. Expectation and related functions
• Note 07. Concentration inequalities
• Note 08. Modes of convergence
• Note 09. Information theory I
• Note 10. Information theory II
• Note 11. Discrete-time Markov chains I
• Note 12. Discrete-time Markov chains II
• Note 13. Discrete-time Markov chains III
• Note 14. Poisson processes
• Note 15. Continuous-time Markov chains
• Note 16. Queueing theory*
• Note 17. Random graphs
• Note 18. Jointly Gaussian random variables
• Note 19. Binary hypothesis testing
• Note 20. MLE and MAP estimation
• Note 21. The Hilbert space of real random variables
• Note 22. Linear least squares error estimation
• Note 23. Minimum mean square error estimation
• Note 24. Kalman filtering
• Note 25. Hidden Markov models*