MAROUN SEMAAN FACULTY OF ENGINEERING AND ARCHITECTURE DEPARTMENT OF Choose a Department COURSE SYLLABUS ENMG 623 Stochastic Processes & Applications Spring 2025-2026

General Course Information

Table 1: Overview of Course Information

General Information

Course Number	ENMG 623
Course Title	Stochastic Processes & Applications
Number of Credits	3
Course Level	graduate
Course Format	Face-to-Face
Pre-requisite Courses	STAT 230
Required/Elective	Elective
Meeting Schedule	T, TH: TR 11:00 am – 12:15 pm
Meeting Location	Bechtel 111

Course Coordinator, Instructors and Teaching Assistants

Table 2: Contact Information for Course Coordinators, Instructors, and Teaching Assistants

Name	Role (Instructor, Coordinator, TA,)	Office Location	Office Hours	Extension	E-mail
Dr. Maher Nouiehed	Coordinator and Instructor	Bechtel 402	3679	mn102@aub.edu.lb
TBA	TA

Course Description

The purpose of this course is to provide students with theoretical knowledge and practical skills required for analyzing probabilistic dynamic systems using stochastic process models. For instance, we can use stochastic processes to model the evolution of a stock price over time, the waiting time of patients in a medical office, the number of people waiting in a bus station, and many other systems that evolve with considerable uncertainty. The course starts with a brief introduction on the concepts of probability, random variables, conditional probability and conditional expectation. Then extends to cover several widely used stochastic processes including Poisson processes, discrete-time Markov chains, continuous-time Markov chains, birth-and-death processes and random walks. Many of these models will be then used to analyze queueing systems.

Course Learning Outcomes

Upon completion of the course, students will be able to:

CLO1. Understand and utilize the concepts of conditional probability and conditional expectation.

CLO2. Understand the basic concepts and applications of Poisson processes.

CLO3. Understand the theory and applications of Discrete and Continuous Markov Chains.

CLO4. Model basic queueing models using Continuous Markov Chains.

CLO5. Establish the ability of utilizing stochastic processes to model real dynamic systems.

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Program Learning Outcomes Addressed in The Course

Explicitly indicate which of the program learning outcomes are addressed in the course.

PLO1. Research Methods: Analyze qualitative and quantitative information while conducting or applying research related to their graduate program.

PLO2. Advanced Core Knowledge in the Discipline: Analyze core concepts and theories in different areas of study in their discipline.

PLO3. Written Communication Skills: Demonstrate scholarly communication skills in clear, detailed and well-structured scientific written format consistent with high standards of their discipline.

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Course Resources and References

Indicate textbook title, author, and year. Include a link to the Moodle page or the course web page.

Introduction to Probability Models, Twelfth Edition, Sheldon Ross, Academic Press, 2023.

Course Outline

Include the topics covered and learning activities (assignments, projects, term papers, etc.) with their due dates. Indicate the alignment of topics/ learning activities with the Course Learning Outcomes.

Table 3: Course Schedule Overview

Week	Date	Topic	Learning Activities and Due Dates	Alignment with CLOs
1	1/19/2026	Introduction to Probability Theory	Lecture	2
2	1/26/2026	Discrete and Continuous Random Variables	Lecture	2
3	2/2/2026	Conditional Probability on Poisson and Binomial Random Variables	Lecture, HW 1 Due Feb 08	2
4	2/9/2026	Conditional Probability Applications and Examples	Lecture, HW 2 Due Feb 22	2
5	2/16/2026	Exponential Distribution	Lecture	2
6	2/23/2026	Poisson Processes: Inter-arrival Distribution, Waiting Time, Conditional on Arrival Times	Lecture	1,2,3
7	3/2/2026	Non-Homogenous Poisson Processes	Lecture, HW 3 Due March 08	1,2,3
8	3/9/2026	Paper Review	Submit Report	1,2,3
9	3/16/2026	Discrete Markov Chains: Classification of States	Lecture	1,2,4
10	3/23/2026	Discrete Markov Chains:Long-Run Proportion, Limiting Probability	Lecture	1,2,4
11	3/30/2026	Continuous-time Markov Chains	Lecture	1,2,4
12	4/6/2026	Birth and Death Process and Random Walks	Lecture, HW 4 Due April 12	1,4
13	4/13/2026	Queuing Models and Applications	Lecture, HW 5 Due April 25	1,4
14	4/20/2026	Final Project Submission and Presentations (Team-Based)	Lecture, Final Project Due April 24
15

Indicate mode of delivery as Lecture, Recitation, Laboratory, Team-based Learning, etc.

Include all methods for assessing student achievement, including assessment rubrics for evaluating the learning activities.

Grading Criteria

Table 4:Evaluation Criteria and Percentage

Type of Evaluation	Percentage
Assignments	20%
Research Paper Review	20%
Project	20%
Final Exam	25%
Class Participation	15%
Total	100%

Course Policies

Attendance

For undergraduate courses:

“Students who miss more than one-fifth of the sessions of any course prior to the withdrawal deadline can be withdrawn from the course by the instructor.”

For graduate courses:

“A student who is absent without excuse for more than one third of the number of sessions in any course may be dropped by the instructor of the course”.

Credit Hour Policy

“This course follows AUB Credit Hour Policy for one credit courses. This course meets twice weekly for 1.25 academic hours per week. The course also includes independent out-of-class assignments for 5 academic hours per week.”

Submissions and Due Dates

Homework problems will be posted on Moodle. Homework assignments must be electronically submitted via Moodle. Only one pdf file should be submitted for every homework assignment. You can submit latex pdf files, word converted pdf files, or scanned images which are converted to pdf. It is encouraged to discuss homework assignments with other students. However, each student is required to submit his/her own personal work. Assignments should be submitted on or prior to the due date. If you have an emergency or other extenuating circumstances and need to turn in an assignment later than its due date, you must contact the instructor before the assignment is due (by email).

Missing Projects/Assignments

Please note that late submissions will start at 90% of the maximum points available and assignment grade will decrease by 2 points for each day thereafter. It should be noted that no assignment will be accepted for credit more than three (3) days after its original due date. Late submissions without pre-clearance will start at 80% of the maximum points available.

Final Examinations:

Please note that students are expected to be on-campus and available to take a final examination for any of their courses at anytime during the final examination period. Students and professors will be notified of the final examination schedule being posted on the Office of the Registrar website as soon as feasible after the change of schedule period (drop and add).

Make-up Quizzes and Exams

Make-up exams will be granted only in exceptional circumstances, such as medical emergencies or serious personal matters. To be eligible for a make-up exam, students must:Notify the instructor in advance (whenever possible) and provide appropriate documentation (e.g., medical report, official letter) within 3 days of the missed exam.Understand that approval of a make-up exam is at the discretion of the instructor and subject to verification of the reason provided. Make-up exams may differ in format, length, and level of difficulty from the original exam. They must be taken within two weeks of the scheduled exam date unless otherwise agreed upon.Unexcused absences from exams will result in a grade of zero for the missed assessment.

Group Work

Student are required to work in groups to build a stochastic model (Poisson Process, Queueing Model, Markov Chain) related to a real-life problem. More details about the project topic will be released during the semester).

Teaching Strategies and Technology Tools

This course combines traditional lectures with active, student-centered learning strategies to enhance engagement and deepen understanding. In addition to lectures, students participate regularly in interactive problem-solving activities, fostering critical thinking and collaborative learning. To encourage consistent participation, 15% of the final grade is allocated to class engagement, rewarding students who contribute actively to discussions and exercises. To support student learning, the course leverages technology tools such as Moodle, where all materials, assignments, and announcements are posted. In addition, recorded videos of lectures and explanations are made available online, allowing students to review concepts at their own pace and revisit challenging topics as needed.

Use of AI in the Course

Artificial Intelligence (AI) tools, including but not limited to ChatGPT, Copilot, and other generative or analytical platforms, may be used in this course under specific guidelines.Permitted Uses: Students may use AI tools to assist in brainstorming, reviewing concepts, checking code, summarizing material, or generating drafts for assignments or projects—provided that all AI-assisted content is critically reviewed, edited, and cited when appropriate.Prohibited Uses: AI tools must not be used to complete graded exams, solve problems intended to assess individual understanding, or submit unedited, AI-generated content as original work. Misuse of AI tools in such contexts will be considered a violation of academic integrity.Educational Purpose: AI tools are encouraged as learning aids to support understanding, promote inquiry, and enhance productivity. Students are expected to remain active participants in the learning process and to demonstrate their own reasoning in all submitted work.Students will also reflect on the benefits and limitations of AI tools as part of class discussions or mini-projects, fostering responsible and informed use of AI in academic and professional settings.

University Rules and Regulations

General Academic Information

Undergraduate Academic Information

Graduate Academic Information

University - Course Withdrawal Policy

A student can withdraw from only one required course per semester. Students who wish to withdraw from more than one required course in any given semester must petition the appropriate faculty committee for permission to do so. Check Withdrawal Information from undergraduate and graduate catalogue.

Academic Integrity (cheating and plagiarism)

Please refer to AUB Student Code of Conduct, in particular section 1.1, which concerns academic misconduct including cheating, plagiarism, in-class disruption, and dishonesty. Please be aware that misconduct is vigorously prosecuted and that AUB has a zero tolerance policy. Course policy is that credible evidence of cheating will result in course failure.

Accessibility Statement to Acknowledge the Unique Learning Needs of Students with Disabilities

AUB strives to make learning experiences as accessible as possible. If you anticipate or experience academic barriers due to a diagnosed disability (including learning disability, mental health, chronic or temporary medical conditions), please contact the Accessible Education Office as soon as possible in order to help establish reasonable accommodations and facilitate a smooth learning process: accessibility@aub.edu.lb; +961-1-350000, x3151; West Hall, 338.

Failure to implement the accommodations provided by the Accessible Education Office constitutes a violation of AUB's non-discrimination policy, and may be reported to the Equal Opportunity Officer/Title IX Coordinator who also serves in the role of Section 504 Coordinator, at 01-350000 ext. 2514, titleix@aub.edu.lb.

Non-Discrimination and Anti-Discriminatory Harassment, including Sexual Harassment at AUB https://www.aub.edu.lb/President/oeo-titleix/Pages/syllabus.aspx

In line with its commitment to the principle of equal opportunity in admission, education, and employment, AUB policies protect you from discrimination and discriminatory harassment, including sex discrimination and sexual harassment, in alignment with the objectives of Title IX of the U.S. Education Amendments of 1972.

The policies are applicable to all the AUB Community including: officers, faculty, staff, academic appointees, students (including medical interns and residents), visiting students, alumni, trainees, visitors, contractors, subcontractors, suppliers, located on campus and at AUB Medical Center, Advancing Research Enabling Communities Center (AREC), or any other facility or program affiliated with the University. The “AUB Community" also includes the dependents and domestic employees of faculty and staff dwelling on campus and at AREC.

If you think you have experienced discrimination, discriminatory harassment, or sexual harassment, we encourage you to inform the Title IX Coordinator, Mitra Tauk at 01-350000 ext. 2514, titleix@aub.edu.lb, report to a Title IX deputy at your faculty or at any other faculty (www.aub.edu.lb/titleix), or report online (www.aub.ethicspoint.com).

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AUB Grading System

https://www.aub.edu.lb/Registrar/Pages/academic-information.aspx

Numeric Course Grade to Letter Course Grade Grade Conversion Chart Table 5:Numeric Course Grade to Letter Course Grade Table 6: Grade Conversion Chart Starting with Numeric Course Grade /100 Corresponding Course Letter Grade Course Letter Grade Quality Points < 60 F A+ 4.3 60 D A 4.0 61–62 D+ A– 3.7 63–65 C– B+ 3.3 66–68 C B 3.0 69–71 C+ B– 2.7 72–74 B– C+ 2.3 75–78 B C 2.0 79–82 B+ C– 1.7 83–86 A– D+ 1.3 87–92 A D 1.0 93–100 A+ F 0.0

Note: The GPA at AUB is capped at 4.0​​

I ​P ​PR ​W NP ​Incomplete ​Pass ​In Progress ​Withdraw ​No Pass