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Course Details

CPSC 6125. Advanced Operating Systems (3-0-3) Issues in the design and functioning of operating systems. Emphasis on synchronization of concurrent activity in both centralized and distributed systems. Deadlock, scheduling, performance analysis, operation system design, and memory systems including distributed file systems.

CPSC 6127. Contemporary Issues in Database Management Systems (3-0-3) This course provides an overview of modern database management systems and issues relating to these systems. Topics include developing a logical model, deriving the physical design, creating data services, creating a physical database, and maintaining a database in a variety of environments.

CPSC 6129. Advanced Programming Languages (3-0-3) Prerequisite: Working knowledge of data structures and discrete mathematics or permission of instructor. A study of the principles, concepts, and mechanisms of computer programming languages-their syntax, semantics, and pragmatics; the processing and interpretation of computer programs; programming paradigms; and language design. Additional topics will include language design principles and models of language implementation.

CPSC 6157. Network and Cloud Management (3-0-3) This course is specifically designed to focus on the protocols, skills and tools needed to support the development and delivery of advanced network and cloud services over the Internet. This graduate-level course is also focused on mastering technical details in a number of areas of advanced networking through reading and hands-on activities of important research topics in the field. The topics covered in this course include 1) network and cloud basics; 2) protocols; 3) network and cloud security; 4) mobile computing; 5) software-defined networking; 6) network and cloud management; 7) data center management; 8) big data analytics and cloud.

CPSC 6185. Intelligent Systems (3-0-3) This course introduces students to the field of Artificial Intelligence (AI) with emphasis on its use to solve real world problems for which solutions are difficult to express using the traditional algorithmic approach. It explores the essential theory behind methodologies for developing systems that demonstrate intelligent behavior including dealing with uncertainty, learning from experience and following problem solving strategies found in nature.