
ECE 302 Probabilistic Methods in Electrical Engineering
Spring 2015
Course Information
 Instructor: Prof. S. Koskie
 Recitation Instructor: Paul Witcher
 Recitations: MW 10:30–11:45am in SL 143
 Recitation Instructor: Keerthanaa Ramesh
 Recitation: T noon–1:15pm in SL 143
 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)
 Final Exam 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 May 17, 2023.
 