COMPUTER ENGINEERING (ENGLISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
EEE5014 Random Processes and Estimation Theory Fall
Spring
3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. SAEID KARAMZADEH
Course Lecturer(s): Dr. Öğr. Üyesi MUSTAFA EREN YILDIRIM
Course Objectives: Course aims to support students in understanding key concepts and results within the elementary probability theory, statistics and stochastic processes.

Learning Outputs

The students who have succeeded in this course;
Upon completion of the course, the student will be able to:
1. Apply probability theory utilizing MATLAB through simulations of experiments with random outcomes.
2. Characterize functions of random variables.
3. Characterize random processes with an emphasis on stationary random processes
4. Obtain the autocorrelation and power spectral density of stationary random processes.

Course Content

Intro to Probability
Probability Theory
Discrete Random Variables
Continuous Random Variables
Pairs of Random Variables
Vector Random Variables
Sums of Random Variables
Estimation
Random Processes
Spectral Density
Kalman Filter
Markov Chains
Queuing Theory

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to Probability Theory
2) Axiomatic Probability Theory
3) Discrete Random Variables
4) Continuous Random Variables
5) Pairs of Random Variables
6) Vector Random Variables Midterm 1
7) Sums of Random Variables
9) Estimation
10) Estimation
11) Midterm 2
12) Random Processes
13) Random Processes
14) Markov Chains

Sources

Course Notes: Leon-Garcia A., Probability, Statistics and Random Processes for Electrical Engineering, 3rd Edition, Pearson Prentice Hall, 2007. ISBN 0-13-715560-3
References: Probability and Random Processes with Applications to Signal Processing, Stark & Woods, Pearson, Third Edition

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 0 % 0
Laboratory 0 % 0
Application 0 % 0
Field Work 0 % 0
Special Course Internship (Work Placement) 0 % 0
Quizzes 0 % 0
Homework Assignments 0 % 0
Presentation 0 % 0
Project 0 % 0
Seminar 0 % 0
Midterms 2 % 60
Preliminary Jury 0 % 0
Final 1 % 40
Paper Submission 0 % 0
Jury 0 % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 10 140
Presentations / Seminar 0 0 0
Project 0 0 0
Homework Assignments 0 0 0
Quizzes 0 0 0
Preliminary Jury 0 0 0
Midterms 1 3 3
Paper Submission 0 0 0
Jury 0 0 0
Final 1 3 3
Total Workload 188

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Define and manipulate advanced concepts of Computer Engineering
2) Use math, science, and modern engineering tools to formulate and solve advenced engineering problems
3) Notice, detect, formulate and solve new engineering problems.
4) Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
5) Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study
6) Work effectively in multi-disciplinary research teams
7) Acquire scientific knowledge
8) Find out new methods to improve his/her knowledge.
9) Effectively express his/her research ideas and findings both orally and in writing
10) Defend research outcomes at seminars and conferences.
11) Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc.
12) Demonstrate professional and ethical responsibility.
13) Develop awareness for new professional applications and ability to interpret them.