Search results: 3159
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: Umar Ahmed
- Teacher: Melike Direkoglu
- Teacher: Parvaneh EsmaIlI
- Teacher: Kamil Yurtkan
- Teacher: Mustafa Cagatayli
- Teacher: Devrim Seral
- Teacher: Devrim Seral
- Teacher: Devrim Seral
- Teacher: Elif Binboga
- Teacher: Ziya Dereboylu
- Teacher: Shihab Ibrahim
- Teacher: Rana Kidak
- Teacher: Mehmet Kusaf
- Teacher: Salahi PehlIvan
- Teacher: Ayse Pekrioglu
- Teacher: Pwadubashiyi PwavodI
- Teacher: Devrim Seral
- Teacher: Kamil Yurtkan
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.
This course explores the history, theory, and application of propaganda and media persuasion techniques. Students will analyze how media is used to influence public opinion, with a focus on political, commercial, and social campaigns. Through examining historical case studies, modern digital media, and ethical considerations, students will gain a deeper understanding of manipulation tactics such as emotional appeals, media framing, and misinformation. The course emphasizes critical thinking, enabling students to identify and deconstruct persuasive messages in various media. By the end of the course, students will develop the skills to both analyze and ethically create persuasive media campaigns.
- Teacher: Eda Akkor