Introduction to artificial intelligence concepts; agent based thinking; uninformed and informed search; constraint satisfaction; knowledge representation; logic; introduction to machine learning and its relation to artificial intelligence; representing uncertainty; markov decision processes; examples from vision, robotics, language and games.

Understanding, implementing, training and debugging deep end-to-end neural network architectures for various tasks of computer vision. Image classification. Loss functions and optimization. Backpropagation. Convolutional neural networks. Recurrent neural networks for video and image analysis. Object detection and segmentation. Generative vision models.

Principles of computer networks and network protocols; Internet protocol stack with emphasis on application, transport, network and link layers; network edge and network core; client/server and peer-to-peer models; routing algorithms; reliable data transfer; flow and congestion control; protocol design and analysis; network performance metrics; software-defined networks; network programming and distributed applications.

Fundamental concepts of parallelism. Overview of parallel architectures, multicores, heterogeneous systems, shared memory and distributed memory systems. Parallel programming models and languages. Multithreaded, message passing, data driven, task parallel and data parallel programming. Design of parallel programs, decomposition, granularity, locality, communication, load balancing, and asynchrony. Performance modeling of parallel programs, sources of parallel overheads.

Threats to data privacy and security; methods for privacy-preserving data collection, analysis, and sharing; data anonymization; differential privacy; security and privacy in machine learning; adversarial machine learning; real- world applications and case studies.

Basic linear models for classification and regression; stochastic gradient descent (backpropagation) learning; multi-layer perceptrons, convolutional neural networks, and recurrent neural networks; recent advances in the field; practical examples from machine translation, computer vision; practical experience in programming, training, evaluating and benchmarking deep learning models.

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.

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.

Understanding, implementing, training and debugging deep end-to-end neural network architectures for various tasks of computer vision. Image classification. Loss functions and optimization. Backpropagation. Convolutional neural networks. Recurrent neural networks for video and image analysis. Object detection and segmentation. Generative vision models.

Fundamental concepts of concurrency, non-determinism, atomicity, race-conditions, synchronization, mutual exclusion. Overview of parallel architectures, multicores, distributed memory. Parallel programming models and languages, multithreaded, message passing, data driven, and data parallel programming. Design of parallel programs, decomposition, granularity, locality, communication, load balancing. Patterns for parallel programming, structural, computational, algorithm strategy, concurrent execution patterns. Performance modeling of parallel programs, sources of parallel overheads.

Threats to data privacy and security; methods for privacy-preserving data collection, analysis, and sharing; data anonymization; differential privacy; security and privacy in machine learning; adversarial machine learning; real- world applications and case studies.

Basic linear models for classification and regression; stochastic gradient descent (backpropagation) learning; multi-layer perceptrons, convolutional neural networks, and recurrent neural networks; recent advances in the field; practical examples from machine translation, computer vision; practical experience in programming, training, evaluating and benchmarking deep learning models.

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.

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

Intensive seminar on selected management topics.

Analysis of Ottoman state, institutions and culture with a specific emphasis on state and social group relations in the nineteenth century Ottoman Empire. Evolution of social change from the Classical Age to the end of the empire, rise of local nationalisms, ruptures and continuities between the Ottoman imperial regime and nation-states.

An applied, non-technical introduction to the methods and ideas of Computational Social Sciences. How new online data sources and the computational methods shed new light on old social science questions and ask brand new questions. Some of the ethical and privacy challenges of living in a world of big data and algorithmic decision making.

This course, broadly speaking, is designed to familiarize the student with Python 3 and advanced data analysis techniques. Core programming concepts using Python, which apply to programming more generally, is covered. These include syntax, data types, functions, loops, recursion, and classes and inheritance. Then, database management, creation, manipulation, and visualization concepts are discussed. A brief overview of Bayesian statistics with an emphasis on practical use in the Stan programming language called through Python will be followed by introductions to the most common machine learning methods. This is a demanding course, with the ultimate goal a final project with an original analysis testing one or several hypotheses. No previous programming experience is assumed. However, a good understanding of linear models is required.

Veri girişi, yönetimi, işleme ve görüntülüme amacıyla Coğrafi Bilgi Sistemleri (CBS) yazılımlarının kullanılması konusunda teknik eğitim. CBS’nin uygulandığı teorik ve pratik çerçeveler. CBS içerisindeki analiz araçlarının arkeoloji, tarih, sanat tarihi, sosyoloji ve göç araştırmaları gibi sosyal ve beşeri bilimlerde jeo-uzamsal önem taşıyan araştırma konularında kullanılması.