The Master and PhD courses to be offered this semester under the names of
Special/Advanced Topics in Computer Engineering are listed below.
| Course Code | : | BLG 609E |
| Course Title | : | Advanced Topics in Comp. Eng. (Neuromorphic Computing) |
| CRN | : | 15371 |
| Instructor | : | Prof. Dr. Burak Berk Üstündağ |
| Day - Time | : | Tuesday, 13:30-16:29 |
| Description | : | Catalog
|
| | | Due to development of AI applications, performing the cognitive functions by using nature inspired computational paradigms has become a major trend for increasing the power and data efficiency. Neuromorphic computing is based on investigation, modeling and emulation of biological neural systems and the brain-connectome structure. This course covers neuromorphic learning in brain-nerve-connectome structures, spiking neural networks and their artificial implementation, neuromorphic coding, basics of stochastic computing, biomimetic neural networks, cognitive functions and neuromorphic system applications. |
Course Code
| : | BLG 553E
|
Course Title
| : | Special Topics in Comp. Eng. (Bioinformatics) |
| CRN | : | 15372
|
Instructor
| : | Assoc. Prof. Dr. Ali Çakmak, Asst. Prof. Dr. Üyesi Mehmet Baysan |
Day - Time
| : | Tuesday, 13:30-16:29 |
| Description | : | Catalog
|
| | | Interactive in-class lectures covering basic concepts of molecular biology and genetics, algorithms for sequence alignment and analysis, genome rearrangements, motif and gene finding, DNA mapping, searching genomes, and systems biology. Students will also practice developing a research paper on a sizeable research problem. Moreover, students will perform critical paper reading in their selected project areas. Finally, students will give presentations summarizing their paper review and research paper components.
|
Course Code
| : | BLG 553E |
Course Title
| : | Special Topics in Comp. Eng. (Casual Inference) |
| CRN | : | 15373
|
| Instructor | : | Dr. Melih Kandemir |
Day - Time
| : | Friday, 09:30-12:29 |
Description
| : | Catalog
|
| | | Introduction to Probability Theory, Probabilistic Graphical Models, D-Separation Rules, Statistical and Causal Models, Assumptions of Causal Inference, Cause-Effect Models, Learning Cause-Effect Models, Multivariate Causal Models, The do-calculus, Hidden Variables, Counterfactual Analysis |