November 13, 2020

EE 399: Probability Models and Inference for Engineering (4 credits)

Instructor: Sreeram Kannan


EE 399A (SLN 13605): Wed/Fri 1:00 – 2:20

Quiz Sections:

EE 399AA (SLN 13606): Tue 9:30 – 11:20

EE 499AB (SLN 13607): Wed 9:30 – 11:20

EE 399AC (SLN 13608): Fri 3:30 – 5:20

Prerequisite: Math 126

Description: This course introduces probabilistic concepts in  conjunction with statistical computation methods. The course draws  examples and motivations from applications throughout Electrical and  Computer Engineering, with topics including reliability, system  engineering, engineering decision-making, and parameter estimation. The  accompanying labs apply concepts and develop skills for modeling and  inference from probabilistic data sets.

This course can be used for satisfying the EE undergrad requirement in  Probability and Statistics.

Learning Objectives:    At the conclusion of the course, the student will

a.  Understand basic axioms of probability and how to compute relevant statistics

b.  Be familiar with basic discrete and continuous random variables

c.  Be able to map engineering problems to appropriate probabilistic models

d.  Be proficient with fundamental data processing principles

e.  Be able to conduct statistical simulation experiments well as

write simple inference algorithms


1. Foundations of Probability

         Sample space, probability, independence, counting

2. Discrete Random Variables

        Probability mass functions, functions of random variables, expectation, joint PMF, conditioning, independence

3. Continuous Random Variables

        Probability density function and cumulative distribution functions, Gaussian, Possion random variables, Conditioning, multiple variables

4. Further topics

        Bivariate Random variables: Covariance, correlation, Inference, Model fitting


1. Introduction to Probability, Bertsekas and Tsitsiklis

Solutions manual available online: (Links to an external site.)

2. Probability with Engineering applicaitons, Bruce Hajek (UIUC notes): (Links to an external site.)

3. (Labs) Think Stats: Probability and Statistics for Programmers ( (Links to an external site.))

Sources for problem solving

1. Textbook problems in “Introduction to Probability, Bertsekas and Tsitsiklis”

Solutions manual available online: (Links to an external site.)

2. There are problems with worked out video solutions in (Links to an external site.)

Search for [Video] in the book above. Example here: (Links to an external site.)


There will be accompanying probability labs that emphasize Monte Carlo simulations as well as performing inference using real data in Python. It will be based partly on the Think Stats book referred here.


Homeworks – 4 (theory) + 4 (simulation)  – 40%

Midterm – 20%

Final – 25%

Class participation – 5%

Quizzes – 10%

Quiz: 15-minute quiz asking brief questions