Course information and syllabus
Set Theory Overview
Lecture: January 13
Lecture: January 15
Lecture: January 17
Lecture: January 22
Lecture: January 24
Lecture: January 27
Lecture: January 29
Lecture: January 31
Lecture: February 3
Lecture: February 5
Lecture: February 7
Lecture: February 10
Lecture: February 12
Lecture: Exam 1 Review
Lecture: February 17
Lecture: February 19
Lecture: February 21
Lecture: February 24
Lecture: February 26
Lecture: February 28
Lecture: March 2
Lecture: March 4
Lecture: March 6
Lecture: March 9
Lecture: March 11
Lecture: Two random variables introduction (after expectation section)
Lecture: Joint cdfs
Lecture: Joint pdfs and pmfs
Lecture: Joint pmfs, jointly Gaussian, and independent rvs
Lecture: Functions of two random variables
Lecture: Two functions of two random variables and joint expectation
Lecture: Joint expectation (continued from previous lecture)
Lecture: Characteristic functions
Lecture: Conditional distributions
Lecture: Conditional distributions (continued from previous lecture)
Lecture: Random processes introduction
Lecture: Mean and autocorrelation functions
Lecture: Wide-sense stationary
Homework 1
Homework 1 Solutions
Homework 2
Homework 2 Solutions
Homework 3
Homework 3 Solutions
Homework 4
Homework 4 Solutions
Homework 5
Homework 5 Solutions
Homework 6
Homework 6 Solutions
Homework 7
Homework 7 Solutions
Homework 8
Homework 8 Solutions
Old Homework 8 Solutions
Homework 9
Homework 9 Solutions
Homework 10
Homework 10 Solutions
Homework 11
Homework 11 Solutions
Sample Exam 1
Sample Exam 1 solutions
Sample Exam 2
Sample Exam 2 solutions
Sample Exam 3
Sample Exam 3 solutions
Sample final exam
Sample final exam solutions