Prerequisite Courses:

Course Language:

English

Courses given by:

Course Objectives:

This is an introductory course designed to introduce the basic concepts and properties of modeling and analysis of probabilistic systems and the student is introduced the means of identifying, formulating and analyzing simple probabilistic models that are commonly faced in engineering practice.
SYE 323 is planned for undergraduate students and intends to develop intuition and model building skills.

Course Content:

1. Probability Theory Review: Probability Theory basics including discrete and continuous Random Variables, Conditional Probability and Conditional Expectations.

2. Markov Chains: Basics, Chapman-Kolmogorov Equations, Limiting Probabilities and applications including Absorbing Chains, Work-Force Planning Models.

3. Exponential Distribution & Poisson Processes: Exponential Distribution and its Properties, Counting Processes, Poisson Processes, Generalization of Poisson Processes.

4. Queuing Theory: Preliminaries, M/M/1 systems with finite or infinite capacity, Network of Queues.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Lab, 4: Case-study

Course Evaluation Methods:

A: Testing, B: Experiment, C: Homework, D: Project