EECS 302: Probabilistic Systems and Random Signals

Quarter Offered

Fall : 9-9:50 MTuWF ; Wei
Spring : 10-10:50 MTuWF ; Guo


Mathematics 234


This course builds a rigorous foundation of probability. Topics covered include: basic concepts of probability theory and statistics, counting, axioms of probability, independence, Bayes rule, continuous and discrete random variables, moments, multiple random variables, conditional distributions, correlation and applications to engineering systems.

This class is an introductory class on probability. Students with some background in probability and may want to consider EECS 395: Probability in Electrical Engineering and Computer Science instead.

REFERENCE TEXTS: Sheldon Ross, A first course in probability (Pearson, ISBN-10: 9332519072)

COURSE INSTRUCTOR: Prof. Ermin Wei (Fall), Prof. Dongning Guo (Spring)


COURSE GOALS: To teach students the basic concepts of probability theory and statistics, random variables, conditional distributions, and correlation as they arise in engineering signal and systems models.

PREREQUISITE: Mathematics 234

COMPUTER USAGE: Matlab Assignments


COURSE OBJECTIVES: When a student completes this course, s/he should be able to:

• Understand the basic concepts of probability and how they arise in engineering systems.

• Analyze systems involving uncertainties using random variable models.

• Understand the concepts of distributions, correlation, and other averaging properties.

• Design simple reliable engineering systems in the presence of errors and failures.

• Understand the basic elements of statistics and analysis of data.

ABET CONTENT CATEGORY: 50% Math and Basic Science, 50% Engineering.