Note that all course materials are copyrighted. Without permission of Prof Chan, students are not allowed to redistribute the materials, including note, homework,
projects and exams.
08-21-2018 Lecture 00: Introduction
08-21-2018 Lecture 00 Supplementary: Installing Python
08-21-2018 Lecture 01: Series
08-23-2018 Lecture 02: Approximation, Integration, Linear Algebra, Set Theory
08-28-2018 Lecture 03: Set Theory, Combinatorics
08-30-2018 Lecture 04: Probability Model, Sample Sapce, Event Space, Axioms
09-04-2018 Lecture 05: Conditional Probabilities
09-06-2018 Lecture 06: Bayes Theorem, Law of Total Probability
09-11-2018 Lecture 07: Probability Mass Function, Expectation
09-13-2018 Lecture 08: Variance, CDF, Bernoulli
09-18-2018 Lecture 09: Bernoulli, Binomial, Geometric, Poisson
09-20-2018 Lecture 10: Poisson, Generating random variable, CDF, Project 1 Discussion
09-25-2018 Lecture 11: PDF, Expectation, Variance, Uniform, Exponential
09-27-2018 Lecture 12: CDF, Gaussian distribution
10-02-2018 Lecture 13: Gaussian, function of random variable
10-11-2018 Lecture 14: Joint PMF, PDF, CDF, Conditional
10-16-2018 Lecture 15: Joint Expectation, Correlation, Conditional Expectation
10-18-2018 Lecture 16: Conditional Expectation
10-23-2018 Lecture 17: Sum of Two Random Variables
10-25-2018 Lecture 18: Joint Gaussian
10-30-2018 Lecture 19: Classification using Joint Gaussians
11-01-2018 Lecture 20: Moment Generating Function
11-06-2018 Lecture 21: Characteristic Function, Law of Large Number
11-13-2018 Lecture 22: Law of Large Number, Central Limit Theorem
11-15-2018 Lecture 23: Intro to random process
11-29-2018 Lecture 24: Auto-correlation, power spectral density
12-06-2018 Lecture 26: Conclusion