imkanlar
13
Eki 2020
Bu Dönem Açılan Special/Advanced Topics LÜ Dersleri

Bilgisayar Mühendisliği Yüksek Lisans ve Doktora programlarında Special/Advancad Topics isimleri altında bu dönem açılacak dersler aşağıda listelenmiştir.


Ders Kodu  : BLG 609E
Ders Adı  : Advanced Topics in Comp. Eng. (Neuromorphic Computing)
CRN  : 15371
Öğretim Üyesi  : Prof. Dr. Burak Berk Üstündağ
Gün - Saat  : Salı, 13:30-16:29
Açıklama   : Katalog
    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.

 

Ders Kodu  : BLG 553E
Ders Adı  : Special Topics in Comp. Eng. (Bioinformatics)
CRN  : 15372
Öğretim Üyesi  : Doç. Dr. Ali Çakmak, Dr. Öğr. Üyesi Mehmet Baysan
Gün - Saat  : Salı, 13:30-16:29
Açıklama   : Katalog
    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.



Ders Kodu  : BLG 553E
Ders Adı  : Special Topics in Comp. Eng. (Casual Inference)
CRN  : 15373
Öğretim Üyesi  : Dr. Melih Kandemir
Gün - Saat  : Cuma, 09:30-12:29
Açıklama   : Katalog
    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