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Course Description: Pattern Recognition is a fundamental area of study within the field of artificial intelligence and machine learning, focusing on the automatic discovery and identification of regularities or patterns in data. This course provides an in-depth exploration of the principles, techniques, and applications of pattern recognition.
Course Objectives:
- To introduce students to the basic concepts and terminology of pattern recognition.
- To familiarize students with various pattern recognition techniques and algorithms.
- To develop students' understanding of the theoretical foundations underlying pattern recognition.
- To enable students to apply pattern recognition techniques to solve real-world problems across diverse domains.
- To provide students with hands-on experience through practical assignments and projects.
Topics Covered:
- Polynomial Regression Analysis
- Logistic Regression (binary, categorical and multi-label classifications)
- Decision Tree
- Ensemble Learning Methods
- Support Vector Machines (SVM)
- Clustering Techniques (Kmeans, SOM)
- Multi-Layer Perceptron (MLP) - Backpropagation Algorithm
- Radial Basis Function Network (RBFN)
- Recurrent Neural Network (LSTM, GRU, Elman Network, Bi-directional layers)
- Deep Convoutional Neural Network (DCNN)
- Transfer Learning
- Teacher: Emre Ozbilge
General concepts of
systems. Discrete and continuous systems. Modelling and simulation of systems. State
variables. Event scheduling. Comparison of analytical and simulation modelling
techniques. General structure of a discrete-event simulation system. Probabilistic
aspects of simulation. Simulation languages and software. Statistical models in
simulation. Random number and random variate generation techniques. Queuing models
in simulation. Input modelling. Verification and validation of simulation
models. Output (statistical) analysis and representation of simulation results.
Applications of simulation.
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
This course provides an overview of the concepts, methods, and tools by which communication research is designed, conducted, interpreted, and critically evaluated. The primary goals
of this course are to help you become a knowledgeable consumer and a limited producer of communication research as you develop skills in gathering, organizing, interpreting and presenting research information using competent and ethically defensible methods.
The following objectives will help you reach these goals:
(1) master the concepts and technical vocabulary of communication research, and be able to use this language appropriately;
(2) comprehend the relationship between theory and research methods in the study of communication as a social science;
(3) assess the ethical choices of researchers in conducting and presenting research;
(4) compare and contrast four major research methods (experimental, survey, textual analysis, and
naturalistic inquiry) used to investigate communication behavior;
(5) develop skills necessary for conducting communication research;
(6) develop the ability to clearly communicate, both orally and in writing, the findings of original communication research to a lay audience; and
(7) become an intelligent consumer of research—able to read, understand, explain and critically evaluate communication and other research reported
in scholarly journals as well as in the popular press.
- Teacher: Dilan Ciftci
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.