COMPUTER ENGINEERING (ENGLISH, NON-THESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
EEE5014 | Random Processes and Estimation Theory | Fall Spring |
3 | 0 | 3 | 8 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Departmental Elective |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Assoc. Prof. SAEID KARAMZADEH |
Course Lecturer(s): |
Assist. Prof. MUSTAFA EREN YILDIRIM |
Recommended Optional Program Components: | None |
Course Objectives: | Course aims to support students in understanding key concepts and results within the elementary probability theory, statistics and stochastic processes. |
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. |
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 |
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 |
Course Notes / Textbooks: | 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 |
Semester Requirements | Number of Activities | Level of Contribution |
Midterms | 2 | % 60 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 14 | 10 | 140 |
Midterms | 1 | 3 | 3 |
Final | 1 | 3 | 3 |
Total Workload | 188 |
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. |