ECE 302: Probabilistic Methods in Electrical and Computer Engineering

Fall 2022
Professor Stanley H. Chan

Lecture Videos and Slides

• This page contains lecture videos and slides.
  

• Please follow the recommended schedule.
  
  

• Lecture 0 Introduction (Slide)
  
  

• • Lecture 1.1 Infinite Series (Video) (Slide)
    

  • Lecture 1.2 Approximation (Video) (Slide)
    

  • Lecture 1.3 Integration (Video) (Slide)
    

  • Lecture 1.4 Linear algebra (Video) (Slide)
    

  • Lecture 1.5 Combinatorics (Video) (Slide)
    
    

• • Lecture 2.1 Set theory (Video) (Slide)
    

  • Lecture 2.2 Probability space (Video) (Slide)
    

  • Lecture 2.3 Axioms of probability (Video) (Slide)
    

  • Lecture 2.4 Conditional probability (Video) (Slide)
    

  • Lecture 2.5 Independence (Video) (Slide)
    

  • Lecture 2.6 Bayes theorem (Video) (Slide)
    
    

• • Lecture 3.1 Random variables(Video) (Slide)
    

  • Lecture 3.2 Probability mass function (Video) (Slide)
    

  • Lecture 3.3 Cumulative distribution function (discrete case) (Video) (Slide)
    

  • Lecture 3.4 Expectation (Video) (Slide)
    

  • Lecture 3.5 Moments and variance (Video) (Slide)
    

  • Lecture 3.6 Bernoulli random variables (Video) (Slide)
    

  • Lecture 3.7 Binomial random variables (Video) (Slide)
    

  • Lecture 3.8 Geometric random variables (Video) (Slide)
    

  • Lecture 3.9 Poisson random variables (Video) (Slide)
    
    

• • Lecture 4.1 Probability density function (Video) (Slide)
    

  • Lecture 4.2 Expectation (continuous) (Video) (Slide)
    

  • Lecture 4.3 Cumulative distribution function (continuous) (Video) (Slide)
    

  • Lecture 4.4 Mean, mode, median (Video) (Slide)
    

  • Lecture 4.5 Uniform random variables (Video) (Slide)
    

  • Lecture 4.6 Exponential random variables (Video) (Slide)
    

  • Lecture 4.7 Gaussian random variables (Video) (Slide)
    
    

  • Lecture 4.8 Transformation of random variables (Video) (Slide)
    

  • Lecture 4.9 Generating random numbers (Video) (Slide)
    
    

• • Lecture 5.1 Joint PMF, PDF, and CDF (Video) (Slide)
    

  • Lecture 5.2 Joint expectation (Video) (Slide)
    

  • Lecture 5.3 Correlation and covariance (Video) (Slide)
    

  • Lecture 5.4 Conditional distributions (Video) (Slide)
    

  • Lecture 5.5 Conditional expectation (Video) (Slide)
    

  • Lecture 5.6 Sum of two random variables (Video) (Slide)
    

  • Lecture 5.7 Examples for sum of two random variables (Video) (Slide)
    
    

• • Lecture 6.1 Moment generating functions (Video) (Slide)
    

  • Lecture 6.2 Characteristic functions (Video) (Slide)
    
    

• • Lecture 10.1 Intro to random processes (Video) (Slide)
    

  • Lecture 10.2 Mean functions (Video) (Slide)
    

  • Lecture 10.3 Autocorrelation functions (Video) (Slide)
    

  • Lecture 10.4 Autocovariance functions, independent processes (Video) (Slide)
    

  • Lecture 10.5 Wide sense stationary processes (Video) (Slide)
    

  • Lecture 10.6 Power spectral density (Video) (Slide)
    

  • Lecture 10.7 Linear time invariant systems (Video) (Slide)
    

  • Lecture 10.8 Mean and autocorrelation through LTI systems (Video) (Slide)
    

  • Lecture 10.9 Cross-correlation through LTI systems (Video) (Slide)