Catalog Course Description

This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.

Fall Term

3 hours of Instructor presentation of course materials per week and 8 hours of Outside Work Hours per week and 1 hours of The exchange of opinions or questions on course material per week.

Spring Term

3 hours of Instructor presentation of course materials per week and 8 hours of Outside Work Hours per week and 1 hours of The exchange of opinions or questions on course material per week.