EE534 CODING THEORY
| Course Code: | 5670534 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Electrical and Electronics Engineering |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Assist.Prof.Dr AHMED HAREEDY |
| Offered Semester: | Fall Semesters. |
Course Objectives
Without effective and smart algorithms for data processing, there would be no data science revolution. The revolution in data storage and data transmission is an interdisciplinary journey where advances in physics and digital design are combined with innovations in coding and signal processing. In this course, students will learn coding theoretic techniques and apply them to modern applications, including digital communications, data storage, cloud storage, and distributed systems supporting machine learning. These techniques encompass fundamentals of finite fields, used in algebraic error-correction codes, message-passing algorithms, used in error-correction codes defined by graphs, and algorithms for lexicographic indexing, used to design constrained codes that mitigate damaging physical effects. Students will learn importance sampling, methods of analyzing iterative algorithms, such as density evolution and EXIT charts, as well as finite-state machines. These algorithms and methods are useful, not just in coding theory, but in many other fields.
Course Content
The arithmetic of Galois fields. Linear block codes with particular emphasis on cyclic codes, such as BCH and RS codes. Convolutional codes. Efficient decoding algorithms for block and convolutional codes. Concatenation and interleaving of codes.
Course Learning Outcomes
Students will learn coding theoretic techniques and apply them to modern applications, including digital communications, data storage, cloud storage, and distributed systems supporting machine learning. These techniques encompass fundamentals of finite fields, used in algebraic error-correction codes, message-passing algorithms, used in graph-based error-correction codes, and algorithms for lexicographic indexing, used to design constrained codes. Students will learn importance sampling, methods of analyzing iterative algorithms, as well as finite-state machines.
Program Outcomes Matrix
| Contribution | |||||
| # | Program Outcomes | No | Yes | ||
| 1 | Depth: Our graduates acquire in depth knowledge in one of the various specialization areas of Electrical and Electronics Engineering, they are informed about current scientific research topics and they implement innovative methods. | ✔ | |||
| 2 | Breadth: Our graduates get familiarized in other subspecialty areas related to their specialization in Electrical and Electronics engineering and/or relevant areas in other disciplines. | ✔ | |||
| 3 | Research: Our graduates acquire the skills to conduct and to complete scientific research by accessing contemporary knowledge in their specialty areas. | ✔ | |||
| 4 | Life-long learning: Our graduates develop their life-long learning habits. | ✔ | |||
| 5 | Communication skills: Our graduates concisely communicate their ideas and work related results in written and oral form. | ✔ | |||
| 6 | Ethics: Our graduates internalize rules of research and publication ethics as well as professional ethics. | ✔ | |||