IC566: Random Signals and Random Processes (Spring 2020)
Instructor:
Kyung-Joon Park (kjp@, x6314, and E3-513)
Office hours: Right after class or by appointment
Class hour and classroom:
Tuesday & Thursday 13:30-15:00 pm, E3-112
Textbooks:
- Introduction to Probability, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2nd edition, 2008
Course Description:
This will be an introductory graduate level course on probability and random processes.
The classes will have three phases:
First, we will begin with the review of undergraduate level probability and random variables.
Second, we will proceed to cover the basic concepts and engineering examples of discrete and continuous random processes, such as Bernoulli, Poisson, and Markov.
Last, we will apply our understanding of probabilistic models to detection and estimation problems of random signals.
Grading policies:
Homework 30%, Midterm exam 30%, Final exam 40%
Announcement:
Lectures (tentative):
- [Feb. 25] No class (textbook reading assignment: Chapter 1.1 & 1.2)
- [Feb. 27] No class (textbook reading assignment: Chapter 1.3 & 1.4)
- [Mar. 3] No class (textbook reading assignment: Chapter 1.5, 1.6, 1.7)
- [Mar. 5] No class (textbook reading assignment: Chapter 2.1 through 2.4)
- [Mar. 10] Probability models and axioms
- [Mar. 12] Conditioning and Bayes rule
- [Mar. 17] Independence & Counting
- [Mar. 19] Discrete random variables; probability mass functions; expectations
- [Mar. 24] Discrete random variable examples; joint PMFs
- [Mar. 26] Multiple discrete random variables: expectations, conditioning, independence
- [Mar. 31] Continuous random variables
- [Apr. 2] Multiple continuous random variables
- [Apr. 7] Continuous Bayes rule; derived distributions
- [Apr. 9] Derived distributions; convolution; covariance and correlation
- [Apr. 14] Iterated expectations; sum of a random number of random variables
- [Apr. 16] Midterm exam
- [Apr. 21] Bernoulli process
- [Apr. 23] Poisson process – I
- [Apr. 28] Poisson process – II
- [Apr. 30] No class (부처님오신날)
- [May 5] No class (어린이날)
- [May 7] Markov chains – I
- [May 12] Markov chains – II
- [May 14] Markov chains – III
- [May 19] Weak law of large numbers
- [May 21] Central limit theorem
- [May 26] Bayesian statistical inference – I
- [May 28] Bayesian statistical inference – II
- [June 2] Classical inference – I
- [June 4] Classical inference – II
- [June 9] Classical inference – III
- [June 11] Final exam