Here you will find all availble EECS courses listed alphabetically. The tabs above futher organize the courses by the starting letter of the course name. If there is a courses that you cannot find listed, or have questions about a course that are not answered by the courses description feel free to Contact Us.
This course is concerned with the application of parallel processing to problems in the natural sciences and engineering. State-of-the-art computing methodologies are studied along with contemporary applications. The course takes a performance-oriented applied approach, with attention to parallel algorithms, parallel system architecture, compilation issues, and system evaluation.
Prerequisite(s): Graduate standing in science or engineering (or consent of instructor) and experience with C, C++, or FORTRAN
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Administration and management of security of information systems and networks, intrusion detection systems, vulnerability analysis, anomaly detection, computer forensics, auditing and data management, risk management, contingency planning and incident handling, security planning, e-business and commerce security, privacy, traceability and cyber-evidence, legal issues in computer security.
Prerequisite(s): EECS 710
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Group discussions of selected topics and reports on the progress of original investigations
Prerequisite(s): Consent of Instructor
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A lecture/laboratory course involving the design and implementation of prototypes of electrical and computer type products and systems. The project specifications require consideration of ethics, economics, manufacturing, and safety.
Prerequisite(s): EECS 420. Co-requisite: EECS 412
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A lecture/laboratory course involving the design and implementation of prototypes of electrical and computer type products and systems. The project specifications require consideration of ethics, economics, manufacturing and safety.
Prerequisite(s): EECS 501
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Fourier signal analysis (series and transform); linear system analysis(continuous and discrete); Z-transforms, analog and digital filter analysis. Analysis and design of continuous and discrete time systems using MATLAB.
Prerequisite(s): EECS 212 and upper-level EECS eligibility
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This course will cover both fundamental and advanced concepts of simulation based analysis and design of communication systems. Monte Carlo simulation principles, modeling techniques, and performance estimation procedures will be discussed. Case studies in simulating satellite, optical and digital microwave links will be presented and the students will be exposed to state of the art simulation packages.
Prerequisite(s): EECS 861 and EECS 862
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Software architecture and design methodologies; architecture business life cycle; architectural styles; software architecture quality attributes; achieving architecture qualities; documenting software architectures; architecture description languages; architecture evaluation methodologies and tradeoff analysis; common architectural patterns; domain specific architectures
Prerequisite(s): EECS 810 and EECS 816
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This course is an introduction to software engineering, and it covers the systematic development of software products. It outlines the scope of software engineering, including life-cycle models, software process, teams, tools, testing, planning, and estimating. It concentrates on requirements, analysis, design, implementation, and maintenance of software products. The laboratory covers CASE tools, configuration control tools, UML diagrams, integrated development environments, and project specific components. Prerequisites: EECS 268 and upper-level EECS eligibility.
Prerequisite(s): EECS 268 and upper-level EECS eligibility
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This course is a continuation of the material presented in EECS 448 on the design and specification phase for production software. It includes a major project which will be carried out as a group effort. Students will be required to specify, design and document, and implement a major component of a combined project.
Prerequisite(s): EECS 448 or equivalent. Not open to students who have taken EECS 810.
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This course focuses on the software engineering tools and practices currently in use in the industry, supporting the complete software development lifecycle. The course provides hands-on experience with current software development tools. Topics include software engineering artifacts, team structure and roles, work contracts, requirements elicitation and analysis, specifications, supplementary specifications, use-case models, activity diagrams, use-case specifications, traceability, technical design, design review meetings, coding standards, code quality, code reviews, and modern software engineering tools.
Prerequisite(s): EECS 448
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Modern techniques for modeling and analyzing software systems. Course coverage concentrates on pragmatic, formal modeling techniques that support predictive analysis. Topics include formal modeling, static analysis, and formal analysis using model checking and theorem proving systems.
Prerequisite(s): EECS 368 or equivalent.
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Process management in the context of software development; building productive teams; measuring performance; management issues in the creation, development, and maintenance of software. Various estimate techniques, planning, risk analysis, project administration and configuration management; fundamentals of software process modeling and definition; process improvement, frameworks for quality software, process properties and measurements, capability maturity evaluation, validation and verification, applications of TQM and SQA to software process improvement
Prerequisite(s): EECS 810
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Software quality engineering as an integral facet of development, from requirements through delivery, maintenance, and process improvement; how to carry out inspections, manual and automated static analysis techniques, fundamental concepts in software testing, verification, validation, test case selection, testing strategies such as black-box testing, white-box testing, integration testing, regression testing, systems testing, acceptance testing; design for testability, fundamental concepts in software integration, configuration management, models for quality assurance; documentation; industry and government standards for quality.
Prerequisite(s): EECS 810
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Objectives, processes, and activities of requirements engineering and requirements management; characteristics of good requirements; types of requirements; managing changing requirements; languages, notations and methodologies; formal and semi-formal methods of presenting and validating the requirements; requirements standards; tracability issues.
Prerequisite(s): EECS 810
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Advanced courses on special topics of current interest in electrical engineering, computer engineering, or computer science, given as the need arises. May be repeated for additional credit.
Prerequisite(s): Variable
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Arranged as needed to present appropriate material to groups of students. May be repeated for additional credit.
Prerequisite(s): Upper-level EECS eligibility and consent of instructor.
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Courses on special topics of current interest in electrical engineering, computer engineering, or computer science, given as the need arises. May be repeated for additional credit.
Prerequisite(s): Variable
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This course presents an introduction to techniques for statically analyzing programs. Coverage includes theoretical analysis, definition and implementation of data flow analysis, control flow analysis, abstract interpretation, and type and effects systems. The course presents both the underlying definitions and pragmatic implementation of these systems.
Prerequisite(s): EECS 662 or EECS 665 or equivalent
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Statistical approaches to processing natural language text have become dominant in recent years. This course is introduction to statistical natural language processing (NLP). The course covers the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students to construct their own implementations. Topics include: word sense disambiguation, clustering, text classification, information retrieval, and other applications.
Prerequisite(s): Fluency in programming and knowledge of basic statistics and probability.
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