Lecture Notes from Berkeley
The following excellent notes are from Berkeley CS 70. They are not perfect in terms of accessibility, but they are the closest I have found in terms of accessibility.
HTML notes
- Counting
- Introduction to discrete probability
- Conditional probability
- Two killer applications (hashing and load balancing)
- Random variables: distribution and expectation
- Variance
- Chebyshev's Inequality
- Some important distributions
- Continuous probability
- Markov chains
PDF notes (untagged) -- these are nearly identical to the above.
- Counting
- Introduction to discrete probability
- Conditional probability and independence
- Killer applications (hashing, coupon collecting and load balancing)
- Random variables: distribution and expectation
- Random variables: Variance and covariance
- Geometric and Poisson Distributions
- Concentration inequalities and LLN
- Continuous probability distributions
- Total expectation, regression
- Markov chains