
ECE 302 Probabilistic Methods in Electrical Engineering
Spring 2015
Course Information
Instructor: Prof. S. Koskie
Recitation Instructor: Paul Witcher
Recitation Instructor: Keerthanaa Ramesh
Lectures: TR 14:30–5:45 am in BS
3015
Textbook:
Probability and Stochastic Processes: A Friendly
Introduction for Electrical and Computer Engineers, 3rd
ed. by Roy D. Yates and David J. Goodman, Wiley, 2014.
There has been
significant reordering of the material, and the number of
examples and homework problems has been increased by about 35
percent in the new edition. Use the 2nd edition at your own
inconvenience.
Grading:
 15% Homework
 50% Midterm Exams (25% each)
 35% Final Exam (noncumulative) Tuesday May 5th,
3:305:30pm
Spring 2015 Course Information Sheet
(Updated January 13,
2015)
 Homework Assignments
due Tuesdays by 4:30pm. For Matlab problems, the
solution must include both the commands, and the output. All
graphs must include grid lines if appropriate, must be properly
labelled, i.e. must have labels on both axes, and must have a
title.
 HW1: all even problems in sections 1.11.3, as assigned
in class (due date 1/20/15)
 HW2: all even problems in sections 1.41.6, as assigned
in class (due date 1/27/15)
 HW3: 2.1.8, 2.1.10, 2.1.12; 2.2.2, 2.2.4, 2.2.8; 2.3.2,
2.3.4; 2.4.2; 2.5.2. Note that it is not necessary to use
tree diagrams to solve these problems so you need not read
Section 2.1. (due date 2/3/15)
 HW4: 3.2.2, 3.2.4, 3.2.8, 3.3.2, 3.3.6, 3.3.8, 3.3.10,
3.5.2, 3.5.8, 3.5.14
(due date 2/10/15)
 HW5: 3.6.2, 3.6.4, 3.6.6, 3.7.2, 3.7.6, 3.7.8, 3.8.4,
3.8.6, 3.8.8, 3.4.2, 3.4.4, 3.4.8
(due date 2/17/15)
 HW6: 4.2.2, 4.3.4, 4.3.6, 4.4.2, 4.4.6, 4.5.4, 4.5.6,
4.5.12, 4.6.14, 4.7.6
(due date 2/24/15)
 HW7: 5.1.6, 5.2.8, 5.3.2, 5.3.4, 5.4.2, 5.5.2, 5.5.4,
8.1.4, 8.1.8, 8.3.2
(due date 3/24/15)
 HW8: 8.4.8, 9.1.2, 9.2.2, 9.3.2, 9.4.6, 9.5.2, 9.5.4,
9.5.6, 9.6.2
(due date 4/02/15)
 HW9: 7.2.2, 7.2.4, 7.2.6, 7.4.4, 7.4.6, 7.4.8, 7.5.2,
7.5.4
(due date 4/09/15)
 HW10: 10.1.2, 10.1.4, 10.2.2, 10.2.4 (you must do
10.2.3 first  don't just copy the solution), 10.2.8,
10.3.4, 10.5.2, 10.5.4.
(due date 4/16/15)
 HW11: 11.1.4, 11.1.6, 13.1.2, 13.2.2, 13.3.2
(due date 4/30/15)
 Optional HW12: 13.4.2, 13.4.4, 13.4.6, 13.5.2,
13.5.4 (a) and (b) only, 13.7.2 (requires 13.2.1),
13.9.2, 13.9.4, 13.9.6
(due by noon on 5/04/15)
Homework Solutions
(Updated May 4, 2015)
Exam Solutions
(Updated April 29, 2015)
Practice Exams
(Updated March 3, 2015)
Some Useful Links:
 Course Objectives:
Upon completion of the course, students should be able to:
 Solve simple probability problems with electrical and computer
engineering applications using the basic axioms of
probability. (Chapters 1
– 5)
 Describe the fundamental properties of probability density and
mass functions with applications to single and multivariate random
variables. (Chapter 3 –
6 and 8)
 Describe the functional characteristics of probability mass
functions frequently encountered in electrical and computer
engineering such as the Bernoulli, Binomial, Geometric, Poisson,
and Uniform. (Chapter
3)
 Describe the functional characteristics of probability density
functions frequently encountered in electrical and computer
engineering such as the Exponential, Gaussian, and Uniform.
(Chapter 4)
 Solve problems involving Derived Random Variables, Conditional
Probabilities, and Sums of Random Variables.
(Chapters 6, 7, and 9,
respectively)
 Determine the first through fourth moments of any probability
density function using the moment generating function.
(Chapter 9)
 Calculate confidence intervals and levels of statistical
significance using fundamental measures of expectation and
variance for a given numerical data set. (Chapters 10)
 Formulate and Test Hypotheses using Maximum A Posteriori
Probability Ratio Test
(Chapter 11)
 Distinguish between random variables and random processes for
given mathematical functions and numerical data
sets. (Chapters 8 and
13)
 Determine whether a random process is ergodic or nonergodic and
demonstrate an ability to quantify the level of correlation
between sets of random processes for given mathematical functions
and numerical data sets.
(Chapter 13)
 Model complex families of signals by means of random processes.
(Chapter 13)
Page last modified August 19, 2023.
 