A capstone design course where students apply engineering and science knowledge in a computer engineering design project. Development, design, implementation and management of a project in teams under realistic constraints and conditions. Emphasis on communication, teamwork and presentation skills.

Theory and practice of 3D computer graphics. Topics covered include graphics systems and models; geometric representations and transformations; graphics programming; input and interaction; viewing and projections; compositing and blending; illumination and color models; shading; texture mapping; animation; rendering and implementation; hierarchical and object-oriented modeling; scene graphs; 3D reconstruction and modeling.

Main problems, datasets, evaluation metrics, and approaches in computer vision for autonomous driving, depth / motion estimation, localization, mapping, free-space estimation, object detection / tracking, semantic / instance segmentation, and end-to-end learning of driving. Credits: 3

Applications of artificial intelligence in user interfaces. Design, implementation, and evaluation of user interfaces that use machine learning, computer vision and pattern recognition technologies. Supporting tools for classification, regression, multi-modal information fusion. Gaze-tracking, gesture recognition, object detection, tracking, haptic devices, speech-based and pen-based interfaces.

Fundamental concepts and current research in natural language processing. Algorithms for processing linguistic information. Computational properties of human languages. Analysis at the level of morphology, syntax, and semantics. Modern quantitative techniques of using large corpora, statistical models, and machine learning applied to problems of acquisition, disambiguation and parsing. Applications such as machine translation and question answering.

Fundamental concepts and recent advances in deep unsupervised learning, autoregressive models, normalizing flow models, variational autoencoders, generative adversarial networks, energy-based models, discrete latent variable models, self-supervised learning, pretraining language

Imaging modalities. Applications and challenges. Medical image segmentation. Feature extraction. Medical image classification. Deep learning for medical images. Convolutional neural networks. Fully convolutional networks. Generative adversarial networks. Multiple-instance learning. Case studies.

Presentation of research topics to introduce the students into thesis research.

Focuses on major approaches and issues in the study of nineteenth century Ottoman and modern Turkish societies. Analyzes major social, economic and political transformations in Ottoman/Turkish society from a regional perspective.

Focuses on selected aspects of nineteenth century Ottoman and modern Turkish political and social structures in comparison to other states and societies. Some of the issues to be covered are state-society relations, migration, social stratification, identities, citizenship and political economic transformations.

Tools and concepts necessary to analyze complex social networks. A range of topics, including the principles of network theory, methods for mapping and measuring social relationships, and the application of statistical techniques for network data. Practical exercises to understand real-world social structures, from small groups to large-scale social systems. Interpretation of network data to uncover patterns and dynamics within social contexts,

Basic concepts of natural language processing and machine learning for text processing will be introduced and case studies on utilizing text mining for social sciences will be studied in the scope of this course. Students will be able to design and conduct computational social science studies using text data and automated processing techniques when they complete this course.

Detailed examination of current topics in CSSM

Network security, Internet and World-Wide Web security, TLS/SSL, firewalls, intrusion detection and prevention systems, security of various Internet and cloud protocols, virtual machine security.

Brief history of artificial intelligence (AI) and robotics, basics of AI and robotics, effects of AI on robotics and automation, potential impact on work and employment, ethical concerns. Computer and network security fundamentals, privacy on the web, mobile device security, personal security, data privacy, password security.

Introduction to cryptographic concepts. Symmetric encryption, the public-key breakthrough, one-way functions, hash functions, random numbers, digital signatures, zero-knowledge proofs, modern cryptographic protocols, multi-party computation. Everyday use examples including online commerce, BitTorrent peer-to-peer file sharing, and hacking some old encryption schemes.

Brief history of artificial intelligence (AI) and robotics, basics of AI and robotics, effects of AI on robotics and automation, potential impact on work and employment, ethical concerns. Computer and network security fundamentals, privacy on the web, mobile device security, personal security, data privacy, password security.

A broad introduction to machine learning covering regression, classification, clustering, and dimensionality reduction methods; supervised and unsupervised models; linear and nonlinear models; parametric and nonparametric models; combinations of multiple models; comparisons of multiple models and model selection.

Applications of data loading, pre-processing, visualization, exploratory data analysis. Using various models for regression and classification such as linear regression, logistic regression, support vector machines, decision trees, random forests, gradient boosted trees, fully connected neural networks. Practical applications of evaluating learning performance, pipelining and model selection. Off-the-shelf dimensionality reduction and clustering methods.