Programming languages concepts and paradigms. Functional programming. Abstraction, encapsulation, type systems, binding, parameter passing, run-time storage, memory, stack, heap, interpreters. Implementation strategies for interpreters. Data representation, sets, syntax, semantics, behavior specification and implementation.
Review of methods and tools used in software development. Object oriented design and open software architectures. Requirements analysis, design, implementation, testing, maintenance and management. Engineering applications.
Hardware organization of computers. Computer components and their functions. Instruction sets, instruction formats and addressing modes. Pipelining and pipeline hazards. Instruction level parallelism. Assembly and machine language. Data and control paths. Computer arithmetic. Floating point representation. Memory hierarchy, cache organization and virtual memory. Parallel architectures.
Microcomputer fundamentals including architecture and operation of a typical microprocessor; bus organization; instruction set; addressing modes; analysis of clocks and timing; interrupt handling; memory (RAM and ROM); DMA, serial and parallel input/output; assembly language programming.
This course covers programming environments and languages over mobile devices. Mobile device architectures and environments, MIDP Application Model, User Interface Libraries, High Level User Interface Components, Low Level User Interface Libraries, MIDP Persistance Libraries. Mobile device operating system environments. Operating Systems such as iPhone OS.
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.
Study of computational models of visual perception and their implementation in computer systems. Topics include: image formation; edge, corner and boundary extraction, segmentation, matching, pattern recognition and classification techniques; 3-D Vision: projection geometry, camera calibration, shape from stereo/silhouette/shading, model-based 3D object recognition; color texture, radiometry and BDRF; motion analysis.
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.
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.
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.
A capstone design project on an industrially relevant problem. Students work on teams in consultation with faculty and industrial members.
Study of computational models of visual perception and their implementation in computer systems. Topics include: image formation; edge, corner and boundary extraction, segmentation, matching, pattern recognition and classification techniques; 3-D Vision: projection geometry, camera calibration, shape from stereo/silhouette/shading, model-based 3D object recognition; color texture, radiometry and BDRF; motion analysis.
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.
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.
Advanced topics in data structures, algorithms, and their computational complexity. Asymptotic complexity measures. Graph representations, topological order and algorithms. Forests and trees. Minimum spanning trees. Bipartite matching. Union-find data structure. Heaps. Hashing. Amortized complexity analysis. Randomized algorithms. Introduction to NP-completeness and approximation algorithms. The shortest path methods. Network flow problems.
Presentation of research topics to introduce the students into thesis research.
Communication with the patient and the caregivers, essential history taking and physical examination practices, requesting goal-directed laboratory tests and interpretation of all patient-related information accurately in the fields of cardiology, pulmonary system diseases and infectious diseases. Common and important medical diseases, signs and symptoms of diseases, laboratory methods and imaging modalities. Acute, chronic diseases and their management.
Intensive seminar on selected management topics.
Some of the most important theoretical questions of the social sciences have been posed by scholars pursuing investigations at the intersection of sociology and history. How are these questions formulated and answered? How important is a consideration of the temporal nature of human actions and social structures and what are its consequences for our understanding of social life? How does the past "matter" to the present? This course addresses these questions and introduces students to some key theories, methodological contributions and a selection of substantive themes in comparative and historical sociology.
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.
Introduces students to social deviance, explores some of the most prominent and important sociological theories of deviance, and reviews the current research on deviance in contemporary society. Offers a comparative perspective on crime and deviance, distribution of power and structures of inequality in the conceptualizations of deviance, and cultural definitions of morality and deviant behavior.