sap course 1490042485

Course Code & Number:

EE 304

Course Title:

Probability and Random Processes

Level of Course:

BS

Credits:

(3+0+0) 3 TEDU Credits, 5 ECTS Credits

Catalog Description:

Basic Concepts of Probability Theory, Discrete Random Variables, One Random Variables, Pairs of Random Variables, Long-Term Averages, Bernoulli and Poisson Random Processes, Analysis and Processing of Random Signals, Markov Chains.

Pre-requisites & Co-requisites:

Pre-requisites: MATH 101
Co-requisites: NONE
Year of Study: 
Junior
Semester: 
Spring
Mode of Delivery: 
Face-to-face
Language of Instruction: 
English
Course Type: 
Compulsory
Required Reading: 
(1) A. Leon-Garcia, “Probability, Statistics, and Random Processes For Electrical Engineering”, Prentice Hall, 3rd Edition, 2008. (2) R. D. Yates and D. J. Goodman, “Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers”, 3rd Edition International Student Version, Wiley, 2014.
Course Objective: 

This course aims to improve the knowledge of students on probability and random processes by providing tools for solving of the engineering problems in communications, signal processing, computer science, and other disciplines.

Extended Description: 

Basic Concepts of Probability Theory, Discrete Random Variables, One Random Variables, Pairs of Random Variables, Long-Term Averages, Bernoulli and Poisson Random Processes, Analysis and Processing of Random Signals, Markov Chains.

Computer Usage: 
MATLAB with Communications System Toolbox https://www.mathworks.com/products/communications.html
Learning Outcomes: 

LO-1: Learn the basic concepts of probability theory (e.g., random experiments, axioms of probability, conditional probability, and statistical independence).
LO-2: Express discrete random variables by using CDFs, PMFs; calculate expected value of random variables. Students also learn Markov and Chebyshev inequalities.
LO-3: Identify continuous random variables, expected values, their joint PDFs, conditional probabilities, conditional expectations, correlation, and covariance.
LO-4: Understand multiple random variables including joint CDFs, PMFs, PDFs; marginal PMFs, PDFs; independent random variables, derived distributions, and conditional probability models.
LO-5: Identify iid sequences, Poisson processes, stationary processes, and cross-correlation.
LO-6: Gain knowledge in power spectral density and response of linear systems to random signals.
LO-7: Understand Markov chains and their transient behavior.

Planned Learning Activities and Teaching Methods: 
Telling/Explaining
Questioning
Reading
Problem Solving
Collaborating
Web Searching
Others
Assessment Methods and Criteria: 
Test / Exam
Quiz/Homework

Student Workload:

Quizzes /Homeworks
21
hrs
Midterm Exam 1
14
hrs
Midterm Exam 2
14
hrs
Final Exam
14
hrs
Others
70
hrs

Prepared By:

Hüseyin Uğur Yıldız

Revised By:

sap_editor