Fall 2020 Professor Stanley H. Chan
This page contains lecture videos and slides.
Please follow the recommended schedule.
Lecture 0 Introduction (Slide)
Week 1 Aug-24
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)
Week 2 Aug-31
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)
Week 3 Sep-7
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)
Week 4 Sep-14
Lecture 3.2 Probability mass function (Video) (Slide)
Lecture 3.3 Cumulative distribution function (discrete case) (Video) (Slide)
Lecture 3.4 Expectation (Video) (Slide)
Week 5 Sep-21
Lecture 3.5 Moments and variance (Video) (Slide)
Lecture 3.6 Bernoulli random variables (Video) (Slide)
Lecture 3.7 Binomial random variables (Video) (Slide)
Week 6 Sep-28
Lecture 3.8 Geometric random variables (Video) (Slide)
Lecture 3.9 Poisson random variables (Video) (Slide)
Midterm 1 on Sep 30, 2020
Week 7 Oct-5
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)
Week 8 Oct-12
Lecture 4.5 Uniform random variables (Video) (Slide)
Lecture 4.6 Exponential random variables (Video) (Slide)
Lecture 4.7 Gaussian random variables (Video) (Slide)
Week 9 Oct-19
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)
Week 10 Oct-26
Lecture 5.2 Joint expectation (Video) (Slide)
Lecture 5.3 Correlation and covariance (Video) (Slide)
Lecture 5.4 Conditional distributions (Video) (Slide)
Week 11 Nov-02
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)
Week 12 Nov-09
Lecture 6.1 Moment generating functions (Video) (Slide)
Lecture 6.2 Characteristic functions (Video) (Slide)
Midterm 2 on Nov 11 (Wed)
Week 13 Nov-16
Lecture A.1 Intro to random processes (Video) (Slide)
Lecture A.2 Mean functions (Video) (Slide)
Lecture A.3 Autocorrelation functions (Video) (Slide)
Lecture A.4 Autocovariance functions, independent processes (Video) (Slide)
Week 14 Nov-23
Lecture A.5 Wide sense stationary processes (Video) (Slide)
Lecture A.6 Power spectral density (Video) (Slide)
Thankgiving break
Week 15 Nov-30
Lecture A.7 Linear time invariant systems (Video) (Slide)
Lecture A.8 Mean and autocorrelation through LTI systems (Video) (Slide)
Lecture A.9 Cross-correlation through LTI systems (Video) (Slide)
The end of the semester