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Sonoma State University
Department of Engineering Science
Course: Engineering Applications of Probability Theory (ES
345E/ES485:01)
Section: 001
Fall, 2014
Instructor: Jack Ou, Ph.D.
Office Location: Salazar Hall 2010B
Telephone: (707) 664 3462
Email: [email protected]
Office Hours: MW 10:30-11:00, TH 3:00 to 4:00 by appointment
Class Days/Time: TTH 4-5:15
Classroom: Salazar Hall 2001
Course Description
Probability and its axioms, conditional probability, sequential experiments, independence, counting,
discrete, continuous distributions, functions of random variables, expectations, multiple random
variables and joint distributions, central limit theorem, weak law of large numbers, estimation of
random variables.
Topics not covered in this course: stochastic process, which is covered in Digital Communication.
Pre-requisite course:
 A satisfactory completion of Calculus II (MATH 211) (≥C).
Recommended course (optional)
 Linear System (ES400)
A chapter-by-chapter list of mathematical concepts used in this course:
 Chapter 1: factorial
 Chapter 2: natural log, exponential function, finite geometric series, infinite geometric series,
 Chapter 3: concepts of limits, fundamental theorem of calculus, integration by parts
 Chapter 4: double integration, double summation.
 Chapter 6: moment generating function (similar to Laplace transform)
Overall Educational Objective:
1.
2.
To develop the logical basis of probability theory
To develop skills necessary to solve practical problems in probability and random processes
Textbook:
(Required) Roy Yates and David Goodman, “Probability and Stochastic Processes: A Friendly Introduction
for Electrical and Computer Engineers,”
Second Edition, 2005. ISBN 978-0-471-27214-4.
(Required) W. Michael Kelly and Robert A. Donnelly, “The Humongous Book of Statistics Problems,”
2009. ISBN 978-1-59257-865-8.
(Optional) Larry Gonick and Woollcott Smith, “The Cartoon Guide to Statistics.”
(Optional) Jim Albert, “Teaching Statistics Using Baseball.”
Useful reference:
https://www.khanacademy.org/math/probability
https://www.khanacademy.org/math/differential-calculus
https://www.khanacademy.org/math/integral-calculus
Reminder: ES230 is a 3 unit course requiring an average of 9-12 hours
of study per week!
POLICIES REGARDING CLASSROOM CONDUCT:

Attendance: You are expected to be in class the entire class time. Please
do not enter
late or leave early. Rare exceptions may be made, particularly in emergency situations.
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Absences: Inform the instructor in advance, if you know you are going to miss a class. Also, take
responsibility for getting missed assignments from other students. Do not expect that you will
be allowed to make up work.
Instructors are not responsible for re-teaching the material you missed due to an absence or
being late.
Conversation: Do not carry on side conversations in class.
Sleep: Do not sleep in class.

Internet browsing: Please turn off all monitors and listen to the lectures.

No texting in class. Please do not check your social media accounts in class (e.g. facebook,
twitter…..etc)
Attitude: You are expected to maintain a civil attitude in class. You may not use inappropriate or
offensive commentary or body language to show your attitude regarding the course, the
instructor, assignments, or fellow students. You are also expected to be courteous to fellow
students when posting comments on Piazza.
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Cell phones and pagers: You may not receive or send telephone calls or use pagers during class.
You are responsible for turning off cell phones and beepers upon entering class.
PLAGIARISM: All forms of cheating and plagiarism are serious offenses that can result in
disciplinary penalties including expulsion from the University. Identical homework submissions
will receive ZERO! This includes copying assignments from the Internet! Refer to the student
handbook for details. Each student is expected to do his/her own work.
SPECIAL NEEDS: If you have emergency medical information that needs to be shared with the
instructor, or require special arrangements in case the building must be evacuated, please
inform the instructor.
Please use the following link to access SSU’s policies on add/drop, cheating and plagiarism,
grade appeal, disabilities, and diversity.
http://www.sonoma.edu/uaffairs/policies/studentinfo.shtml
POLICIES REGARDING CLASSROOM CONDUCT:
 HOMEWORK: All students are required to complete homework assignments. Homework
assignments must be submitted in
the beginning of the class on the due
date. Late submissions will NOT BE ACCEPTED! All hardcopy submissions must be
stapled and have a cover sheet, otherwise they will not be accepted. Please avoid printing
your homework when class starts! Homework assignments are designed to enhance students’
understanding of the course materials. They are hard! Each assignment will take an average
six hours. Please do not wait till the last minute to work on the homework assignments.

BLOGS: Each individual student is required to sign up with PIAZZA You must login using your first
and last name (e.g., Cesar Chavez). Make sure you signup as STUDENT!

EXAMS: Exams will consist of problems designed to test your understanding of the concepts
covered in class and lab. Anyone missing an exam will receive a zero grade for that exam.
Exams are graded promptly. No make-up is allowed. There may be pop quizzes from time to
time at the discretion of the instructor.
GRADING
 Three exams (total: 60 %. Test 1=10 %, test #2=20%, and final=30 %)
 Class participation (total: 10 %. Attendance=5%, participate in active learning exercise both in
class and out of class=5%)
 Project (10 %)
 Homework assignment: 20 %
Note:
 To encourage students to complete SETE, the final course grade will be made available by 12/12
to students who provide evidence of completing SETE by the due date.
Important dates:
 9/3 last day to drop a course with adjusted fees (done on-line)
 9/4-15: drop with a “W” (done on-line)
 9/16-11/13: petition to withdraw from a class with 20 administrative fee (dropping classes
permitted because of serious and compelling reasons)
Software/Account:
1. A Moodle/Seawolf account
2. Piazza
Tentative schedule
#
1
2
Date
8/19
8/21
Day
Tuesday
Thursday
3
8/26
Tuesday
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
8/28
9/2
9/4
9/9
9/11
9/16
9/18
9/23
9/25
9/30
10/2
10/7
10/9
10/14
10/16
10/21
10/23
10/28
10/30
11/4
11/6
11/11
11/13
11/18
11/20
11/25
11/27
12/2
12/4
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Tuesday
Thursday
Topic
Math assessment and intro
Set theory, probability axioms, independence,
conditional probability
Sequential Experiments, tree diagrams, sampling
without replacement
Sampling with replacement, independent trial, reliability
Problems
Test #1: chapter 1
PMF &binomial RV
Poisson RV
CDF + averages
Problems
CDF, PDF ,Expected values
Families of continuous RV: Uniform and exponential
Gaussian RV
Probability models of derived RV
Conditioning a continuous RV, matlab
Problems
Test #2: chapter 2 and 3
joint CDF, joint PMF, marginal PMF
Joing PDF, marginal PDF
functions of two RVs
expected values
Conditioning by an event, by a RV
Problems
Expected value of sums, PDF of the sum of 2 RVs
MGF and MGF of the Sums of independent RV
Random Sums of independent RV
Central limit theorem+applications
The Cernoff Bound+problems
A hands-on tutorial on confidence interval
Thanksgiving!
A hands-on tutorial on hypothesis testing
Review for the final exam
Yates
K&D
1.2-1.5
4
1.6-1.8
5
1.8-1.10
1
5
2.1-2.3
2.3
2.4-2.5
2
3.1-3.3
3.4
3.5
3.7
3.8,3.9
4
6
6
6
6
7
7
7
7
7
4.1-4.3
4.4-4.5
4.6
4.7
4.8
4
6.1-6.2
6.3-6.4
6.5
6.6-6.7
6.8
7
8
8
9
8
10
A student who successfully fulfills the course requirements will have demonstrated:
A. an ability to describe a random experiment in terms of an procedure, observation, and a
probability model.
B. an ability to characterize probability models by employing counting methods and basic
probability mass function and probability density function canonical models for discrete and
continuous random variables.
C. an ability to evaluate first and second moments and cumulative distribution function for both
discrete and continuous random variables
D. an ability to characterize functions of random variables
E. an ability to characterize jointly multiple discrete and continuous random variables
F. an ability to describe conditional and independent events and conditional random variables.
G. an ability to describe independent events and independent random variables and their sums.
H. an ability characterize stochastic processes with an emphasis on stationary random processes.
Course Learning
Objectives
Level of
Support
Knowledge of probability and statistics
A-H
5
Knowledge of mathematics through differential and integral calculus, basic
sciences, and engineering sciences necessary to analyze and design complex
devices
A-H
1
Systems containing hardware and software components
A,B
2
EE Program Criteria
[Support Level (0-5) 0=No support, 1=lowest support, 5 =highest support]
ABET Student Outcomes
(a) an ability to apply knowledge of mathematics, science, and engineering
Course Learning
Objectives
Level of
Support
A-H
5
(b) an ability to design and conduct experiments, as well as to analyze and
interpret data
0
(c) an ability to design a system, component, or process to meet desired
needs
0
(d) an ability to function on multi-disciplinary teams
0
(e) an ability to identify, formulate, and solve engineering problems
A,B,C,D
3
(f) an understanding of professional and ethical responsibility
0
(g) an ability to communicate effectively
0
(h) the broad education necessary to understand the impact of engineering
solutions in a global and societal context
0
(i) a recognition of the need for, and an ability to engage in life-long learning
0
(j) a knowledge of contemporary issues
0
(k) an ability to use the techniques, skills, and modern engineering tools
necessary for engineering practice
0
Course Quality Assessment: Students survey and feedbacks are used to improve the course quality.