Search results: 3222
- Teacher: Omar Alotabi
- Teacher: Mustafa Ambar
- Teacher: Ozcel Cangul
- Teacher: Parvaneh EsmaIlI
- Teacher: Aisha Ghufran
- Teacher: Meshack Ishaya
- Teacher: Ahmed Mohammed
- Teacher: Eliane Mpabadashima
- Teacher: Kamil Yurtkan
- Teacher: Ibrahim Abdullahi
- Teacher: Basmah Anber
- Teacher: Naila Farooq
- Teacher: Wajdi Hassan
- Teacher: Rahaf Ismail
- Teacher: Shahrzad JazI
- Teacher: Emre Ozbilge
- Teacher: Sara Salehi
- Teacher: Ouahiba Staf
- Teacher: Oyku Akaydin
- Teacher: Aisha Ghufran
- Teacher: Eliane Mpabadashima
- Teacher: Gorkem Akdur
- Teacher: Fatma Tansu Hocanin
- Teacher: Basmah Anber
- Teacher: Basmah Anber
- Teacher: Wajdi Hassan
- Teacher: Shahrzad JazI
- Teacher: Ashkan Mohebali
- Teacher: Elnaz Mohebbi
- Teacher: Soheila Saberi
The course has an overview of discrete time signals and systems. Sampling/Reconstruction
principles both in time and frequency domains. The Z-Transform and its
properties. Structure of discrete time systems; tapped delay or lattice etc.
Digital filter designs. Realization of digital filters (FIR and IIR). The
discrete Fourier and inverse Fourier transforms. The fast Fourier transform
(FFT) and its analysis. Image acquisition, sampling and quantization. Image
enhancement: Spatial and frequency domain techniques. Image restoration:
Inverse, Wiener and mean filtering. Image compression: Compression models.
- Teacher: Huseyin Oztoprak
- Teacher: Basmah Anber
- Teacher: Safa Awadallah
- Teacher: Mustafa Buzun
- Teacher: Abdul Salam
- Teacher: Devrim Seral
- Teacher: Devrim Seral
- Teacher: Behnam Shahriari
- Teacher: Behnood Tabrizi
- Teacher: Melike Direkoglu
- Teacher: Wajdi Hassan
- Teacher: Rahaf Ismail
- Teacher: Salma Lahlali
- Teacher: Ouahiba Staf
- Teacher: Safa Awadallah
- Teacher: Felix Babalola
- Teacher: Aisha Ghufran
- Teacher: Salma Lahlali
- Teacher: Eliane Mpabadashima
- Teacher: Mahmoud Rob
- Teacher: Devrim Seral
- Teacher: Ertan Akun
- Teacher: Keyvan Bahlouli
- Teacher: Huseyin NasIfoGlu
- Teacher: Devrim Seral
Principles of computer network design. Network design and optimization algorithms. Centralized network design, switching node location problems. Application of minimum spanning tree and shortest path algorithms to problems in network design. Static and dynamic routing algorithms. Network reliability analysis in design. Ad-hoc and cellular wireless network design. Topics in computer network performance analysis. Case studies.
In this course, fundamental concepts and applications of information retrieval and natural language processing will be discussed. Students will gain knowledge on information retrieval (IR) techniques (tf-idf, BM25, etc.), data models (i.e. vector space model, probabilistic models, word2vec, GloVe) for IR, Semantic Web for data representation and retrieval. In addition, students will gain knowledge on theory (i.e. stemming, morphological, syntactic, semantic analysis, etc.) and applications (i.e. document clustering, question answering, sentiment analysis, etc.) of Natural Language Processing (NLP). Furthermore, students will investigate recent trends of deep learning for IR and NLP, such as neural networks, transformers, attention networks, graph convolutional neural networks, LSTM and Bidirectional Encoder Representations from Transformers (BERT). By the end of the course students will learn both theoretical and applications of IR and NLP. In addition, student will choose a topic, and will have hands-on experience by developing a project in a relevant field.