Course Structure Diagram with Credits
To see the course details (such as objectives, learning outcomes, content, assessment and ECTS workload), click the relevant Course Code given in the table below.
|
1. Year Fall Semester
|
| Code |
Pre. |
Course Name |
Theory |
App/Lab |
Local Credits |
ECTS |
|
ELEC 001
|
|
Elective Course I |
3 |
0 |
3 |
7.5 |
|
ELEC 002
|
|
Elective Course II |
3 |
0 |
3 |
7.5 |
|
GS 593
|
|
Research Design and Methods in Engineering |
3 |
0 |
3 |
7.5 |
|
POOL 008
|
|
Theoretical Courses |
3 |
0 |
3 |
7.5 |
| Total : |
30 |
|
1. Year Spring Semester
|
| Code |
Pre. |
Course Name |
Theory |
App/Lab |
Local Credits |
ECTS |
|
ELEC 003
|
|
Elective Course III |
3 |
0 |
3 |
7.5 |
|
ELEC 004
|
|
Elective Course IV |
3 |
0 |
3 |
7.5 |
|
GS 595
|
|
Seminar |
0 |
0 |
0 |
7.5 |
|
POOL 009
|
|
System Courses |
3 |
0 |
3 |
7.5 |
| Total : |
30 |
|
2. Year Fall Semester
|
| Code |
Pre. |
Course Name |
Theory |
App/Lab |
Local Credits |
ECTS |
|
GS 599
|
|
Master Thesis |
0 |
0 |
0 |
30 |
| Total : |
30 |
|
2. Year Spring Semester
|
| Code |
Pre. |
Course Name |
Theory |
App/Lab |
Local Credits |
ECTS |
|
GS 599
|
|
Master Thesis |
0 |
0 |
0 |
30 |
| Total : |
30 |
|
Elective Courses
|
| Code |
Pre. |
Course Name |
Theory |
App/Lab |
Local Credits |
ECTS |
|
CE 531
|
|
Machine Learning |
3 |
0 |
3 |
7.5 |
|
CE 532
|
|
Applied Quantum Machine Learning |
3 |
0 |
3 |
7.5 |
|
CE 533
|
|
Artificial Intelligence |
3 |
0 |
3 |
7.5 |
|
CE 534
|
|
Intelligent Agents and Multi Agent Planning |
3 |
0 |
3 |
7.5 |
|
CE 535
|
|
Software Engineering for Real-Time Systems |
3 |
0 |
3 |
7.5 |
|
CE 536
|
|
Human-Computer Interaction |
3 |
0 |
3 |
7.5 |
|
CE 603
|
|
Advanced Distributed Database Systems |
3 |
0 |
3 |
7.5 |
|
CE 604
|
|
Advanced Computer Graphics |
3 |
0 |
3 |
7.5 |
|
CE 605
|
|
Wireless Sensor Networks |
3 |
0 |
3 |
7.5 |
|
CE 606
|
|
Video Coding and Decoding |
3 |
0 |
3 |
7.5 |
|
CE 607
|
|
Information Security |
3 |
0 |
3 |
7.5 |
|
CE 608
|
|
Formal Specification and Verification of Concurrent Systems |
3 |
0 |
3 |
7.5 |
|
CE 609
|
|
Advanced Numerical Analysis |
3 |
0 |
3 |
7.5 |
|
CE 610
|
|
Sparse Approximation Algorithms |
3 |
0 |
3 |
7.5 |
|
CE 611
|
|
Design Patterns and Code Refactoring |
3 |
0 |
3 |
7.5 |
|
CE 612
|
|
Software Evolution and Maintenance |
3 |
0 |
3 |
7.5 |
|
EEE 501
|
|
Applied Digital Image Processing |
3 |
0 |
3 |
7.5 |
|
EEE 502
|
|
Pattern Recognition |
3 |
0 |
3 |
7.5 |
|
EEE 504
|
|
Bankaları / Wavelets and Filter Banks |
3 |
0 |
3 |
7.5 |
|
EEE 505
|
|
Biomedical Signals and Instrumentations |
3 |
0 |
3 |
7.5 |
|
EEE 506
|
|
Adaptive Signal Processing |
3 |
0 |
3 |
7.5 |
|
EEE 509
|
|
Selected Topics in Signal Processing |
3 |
0 |
3 |
7.5 |
|
EEE 511
|
|
Artificial Neural Networks for Signal Processing and Control |
3 |
0 |
3 |
7.5 |
|
EEE 512
|
|
Optimal Control |
3 |
0 |
3 |
7.5 |
|
EEE 527
|
|
Principles of Autonomous Vehicle Design |
3 |
0 |
3 |
7.5 |
|
EEE 533
|
|
Digital VLSI Design |
3 |
0 |
3 |
7.5 |
|
EEE 542
|
|
Detection and Estimation Theory |
3 |
0 |
3 |
7.5 |
|
EEE 543
|
|
Basics of Wireless Communications |
3 |
0 |
3 |
7.5 |
|
EEE 561
|
|
Microprocessor Systems |
3 |
0 |
3 |
7.5 |
|
EEE 562
|
|
Real-Time Signal Processing |
3 |
0 |
3 |
7.5 |
|
EEE 601
|
|
Fast Filtering Algorithms |
3 |
0 |
3 |
7.5 |
|
EEE 602
|
|
Video Processing |
3 |
0 |
3 |
7.5 |
|
EEE 612
|
|
Chaos and Fractals |
3 |
0 |
3 |
7.5 |
|
EEE 652
|
|
Stochastic Processes |
3 |
0 |
3 |
7.5 |
|
IE 509
|
|
Heuristics |
3 |
0 |
3 |
7.5 |
|
IE 510
|
|
Discrete Optimization |
3 |
0 |
3 |
7.5 |
|
IE 513
|
|
Mathematical Programming and Applications |
3 |
0 |
3 |
7.5 |
|
IE 520
|
|
Constraint Programming |
3 |
0 |
3 |
7.5 |
|
IE 530
|
|
Evolutionary Algorithms |
3 |
0 |
3 |
7.5 |
|
IE 532
|
|
Advanced Scheduling Systems |
3 |
0 |
3 |
7.5 |
|
IE 590
|
|
Advanced Topics in IE and OR |
3 |
0 |
3 |
7.5 |
|
MATH 504
|
|
Statistics |
3 |
0 |
3 |
7.5 |
|
MATH 553
|
|
Optimization |
3 |
0 |
3 |
7.5 |
|
MATH 554
|
|
Basic Topics in Mathematics |
3 |
0 |
3 |
7.5 |
|
MATH 602
|
|
Advanced Linear Algebra and Optimization |
3 |
0 |
3 |
7.5 |
|
MATH 658
|
|
Advanced Data Analysis |
3 |
0 |
3 |
7.5 |
|
MATH 659
|
|
Graph Theory |
3 |
0 |
3 |
7.5 |
|
MATH 662
|
|
Cryptography |
3 |
0 |
3 |
7.5 |
|
MATH 663
|
|
Biomathematics |
3 |
0 |
3 |
7.5 |
|
MATH 667
|
|
Theory of Finite Elements |
3 |
0 |
3 |
7.5 |
|
MATH 668
|
|
Spectral Analysis of Differential Operators |
3 |
0 |
3 |
7.5 |
|
MATH 671
|
|
Fuzzy Optimization |
3 |
0 |
3 |
7.5 |
|
POOL 008 - Theoretical Courses
|
| Code |
Pre. |
Course Name |
Theory |
App/Lab |
Local Credits |
ECTS |
|
CE 518
|
|
Advanced Computing Theory |
3 |
0 |
3 |
7.5 |
|
CE 601
|
|
Advanced Algorithms |
3 |
0 |
3 |
7.5 |
|
POOL 009 - System Courses
|
| Code |
Pre. |
Course Name |
Theory |
App/Lab |
Local Credits |
ECTS |
|
CE 513
|
|
Advanced Operating Systems |
3 |
0 |
3 |
7.5 |
|
CE 516
|
|
Advanced Computer Networks & Communication |
3 |
0 |
3 |
7.5 |
|
CE 602
|
|
Advanced Computer Architecture |
3 |
0 |
3 |
7.5 |
| Additional Notes |
EXPLANATIONS
The students are required to take a total of 120 ECTS in order to complete the master program with thesis.
In order to graduate from the master program with thesis, the students are required to take 6 courses (one from Theory Pool, one from System Pool, and four Elective Courses two of which must be CE coded) in addition to GS 595 - Seminar, and GS 599 - Master Thesis courses.
Students may take one elective course with code prefixed with "CE" or "SE" (provided that the course has 7.5 ECTS, 3 IUE Credits) from undergraduate level elective courses, unless they took the course during their undergraduate education.
|
If you need support for these courses due to your disability, please refer to Disability Support Unit. Contact; engelsiz@ieu.edu.tr |
