You may also be interested in my annotated course lectures for Digital Signal Processing, Introduction to Image Processing, and Computer Vision for Visual Effects. I also have roughly hour-long lectures on Engineering Probability at this link, but I think these short lectures are better.
PB 0: Introduction
PB 1: Experiments and Sample Spaces
PB 2: Events
PB 3: Axioms of Probability
PB 4: Discrete Sample Spaces
PB 5: Combinatorics
PB 6: Combinatorics Practice Problems
PB 7: Continuous Sample Spaces
PB 8: Conditional Probability
PB 9: The Total Probability Theorem
PB 10: Bayes' Rule
PB11: A Medical Testing Example
PB12: The Monty Hall Problem
PB13: Independent Events
PB14: Bernoulli Trials
PB15: Binomial and Geometric Practice Problems
PB16: Bernoulli's Theorem
PB17: Discrete Random Variables
PB18: Probability Mass Function
PB19: The Poisson Random Variable
PB20: Expected Value for Discrete Random Variables
PB21: Expected Value of Functions
PB22: The Variance
PB23: Conditional Probability Mass Functions
PB24: The Memoryless Property
PB25: Conditional Expected Value
PB26: Cumulative Distribution Functions
PB27: Continuous Random Variables
PB28: Probability Density Functions
PB29: The Exponential Random Variable
PB30: The Gaussian Random Variable
PB31: Q Function Practice Problems
PB32: Expected Value for Continuous Random Variables
PB33: Expected Value of Functions of a Random Variable
PB34: Expected Value Practice Problems (Using Integration)
PB35: Expected Value Practice Problems (Using Properties)
PB36: Designing a Quantizer
PB37: One-to-One Functions of a Random Variable
PB38: Many-to-One Functions of a Random Variable
PB39: Markov and Chebyshev Inequalities
PB40: Two Discrete Random Variables
PB41: Joint PMF/CDF for Discrete Random Variables
PB42: The Marginal PMF for Discrete Random Variables
PB43: Joint PDF/CDF and Marginals for Continuous Random Variables
PB44: Joint Random Variable Practice Problems
PB45: The Joint Gaussian Random Variable
PB46: Independence of Random Variables
PB47: Joint Expectations and Covariance
PB48: The Correlation Coefficient
PB49: Conditional PMFs for Discrete Random Variables
PB50: Class-Conditional Probability Density Functions
PB51: The Bayes Decision Rule
PB52: Conditional PDFs for Continuous Joint Random Variables
PB53: Conditional Gaussian Distributions
PB54: The Law of Iterated Expectation
PB55: Conditional Expectation Practice Problems
PB56: More Conditional Expectation Practice Problems
PB57: Sums of Random Variables
PB58: Laws of Large Numbers
PB59: The PDF of a Sum of Random Variables
PB60: Transformations of Random Variables
PB61: The Central Limit Theorem
PB62: Central Limit Theorem Practice Problems
PB63: Weak Law of Large Numbers vs. Central Limit Theorem
PB64: Confidence Intervals
PB65: Maximum A Posteriori (MAP) Estimation
PB66: Maximum Likelihood Estimation
PB67: Minimum Mean-Square Estimation
PB68: Linear Minimum Mean-Square Estimation
PB69: Significance Testing
PB70: Hypothesis Testing
PB71: A Hypothesis Testing Example
PB72: Testing the Fit of a Distribution
PB73: Generating Samples of a Random Variable
PB74: Tips and Tricks for Random Number Generation
Engineering Probability by Rich Radke is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Based on a work at http://www.ecse.rpi.edu/~rjradke/probcourse.html.
Permissions beyond the scope of this license may be available at this contact page.
