This course is a general introduction to programming using the Java programming language. It emphasizes the structured programming language aspects of Java and de-emphasizes its object-oriented aspects. The latter are covered only to the extent that enables students to use standard Java libraries for common tasks. Students who complete this course successfully should gain a solid foundation in algorithmic thinking and structured programming, and should be able to perform basic, common computational tasks easily and efficiently.

This course is a general introduction to programming using the Java programming language. It emphasizes the structured programming language aspects of Java and de-emphasizes its object-oriented aspects. The latter are covered only to the extent that enables students to use standard Java libraries for common tasks. Students who complete this course successfully should gain a solid foundation in algorithmic thinking and structured programming, and should be able to perform basic, common computational tasks easily and efficiently.

This course is a general introduction to programming using the Java programming language. It emphasizes the structured programming language aspects of Java and de-emphasizes its object-oriented aspects. The latter are covered only to the extent that enables students to use standard Java libraries for common tasks. Students who complete this course successfully should gain a solid foundation in algorithmic thinking and structured programming, and should be able to perform basic, common computational tasks easily and efficiently.

Object oriented programming using Java. Data types, expressions, control statements, strings, arrays. Classes, objects, methods, overloading, variable scope, memory. Recursion. Inheritance, polymorphism, abstract classes, interfaces, nested classes, anonymous classes. Exception handling. Strings and regular expressions. File I/O. Generic collections. Generic classes and methods. Lambdas and streams. Event-driven programming. Multithreading.

Basic data structures, algorithms, and their computational complexity. List, stack, queue, priority queue, map, tree, balanced tree, hash table, heap, skip list, trie, graph. Basic search, selection, sorting, and graph algorithms. Recursion.

Engineering problem solving and design using C programming language. The course will cover basic syntax/grammar and concepts of C programming language, including control flow, functions and modular programming, static and global variables, pointers and memory addressing, arrays and pointer arithmetic, strings and searching and sorting, followed by example engineering analysis and design problems, including electric circuit analysis, digital signal processing, machine learning, finance, and data analysis.

Introduction to operating systems concepts, process management, memory management, virtual memory, input-output and device management, file systems, job scheduling, threads, process synchronization, deadlocks, interrupt structures, case studies of operating systems.

Conceptual and practical aspects of databases and database management systems. Entity-relationship model, relational model, relational algebra, Structured Query Language (SQL), normal forms and normalization, transaction management, scheduling and serializability, concurrency control and locking, indexing, recent trends in databases and NoSQL.

Sound and human speech systems, phonetics and phonology, speech signal representations, role of pitch and formants, pitch-scale and time-scale modifications, basics of speech coding and VoIP systems, fundamentals of pattern and speech recognition, search algorithms for speech recognition.

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.

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.

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.

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.

Sound and human speech systems, phonetics and phonology, speech signal representations, role of pitch and formants, pitch-scale and time-scale modifications, basics of speech coding and VoIP systems, fundamentals of pattern and speech recognition, search algorithms for speech recognition.

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.

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.

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.

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

Multidisciplinary approach to the diagnosis and treatment of cardiovascular and respiratory diseases: Physical examination of the heart, arteries and veins, embryology, anatomy, physiology, pathogenesis, diagnostic methods, medical, interventional and surgical treatment modalities will be evaluated. Topics include: Electrocardiography, rhythm disturbances, atherosclerosis and hyperlipidemia; Ischemic and valvular heart diseases; Cardiac traumas, coagulation, infective endocarditis, myocarditis, pericarditis, cardiac tumors; Traumatic, thromboembolic, cerebrovascular, lower occlusive, vasospastic, aneurysmatic arterial diseases; venous insufficiency; myocardial protection, congenital heart diseases, thoracic aortic diseases will be discussed. Diseases of the respiratory system including pulmonary vascular disorders, pulmonary embolism and hypertension, sleep disorders, neoplasms of the respiratory system, diagnosis and tretament of anaphylaxis will be evaluated.

Intensive seminar on selected management topics.

Intensive seminar on selected management topics.

Introduces the fundamentals of historical and social research by focusing on a variety of research methods. Exposure to the philosophy of social science methodology and quantitative research methods. Introduction to historical, sociological, and comparative methods, including oral history, ethnography, interviewing techniques, archival research and document analysis. Building on their training in these methods, students are guided through the steps of research design, namely writing research proposals, constructing hypotheses, operationalizing research questions, designing questionnaires and interview forms, research and publication ethics and data collection.

Surveys some of the main themes and names in social theory. Examines in depth the classical foundations of sociological theory, especially the works of Marx, Weber and Durkheim. Focuses on some of the important early and late twentieth-century thinkers, including Gramsci, Bourdieu and Foucault, and discusses the feminist and postcolonial challenges to classical theory.