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.
Introduction to the concept of databases and their operations. Basic database concepts, architectures, and data storage structures and indexing. Though other architectures are discussed, focus is on relational databases and the SQL retrieval language. Normalization, functional dependencies, and multivalued dependencies also covered. Culminates in the design and implementation of a simple database with a web interface.
Prerequisite(s): EECS 448 or consent of instructor. Students cannot receive credit for both EECS 647 and EECS 746.
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Extracting data from data bases to data warehouses. Preprocessing of data: handling incomplete, uncertain and vague data sets. Discretization methods. Methodology of learning from examples: rules of generalization and control strategies. Typical learning systems: ID3, AQ, C4.5 and LERS. Validation of knowledge. Visualization of knowledge bases. Data mining under uncertainty, using approaches based on probability theory, fuzzy set theory and rough set theory.
Prerequisite(s): Graduate standing in CS or CoE or consent of instructor.
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Data abstraction and abstract data types. Topics include the design and implementation of dictionary, priority queues, concatenated queue, disjoint set structures, graphs, and other advanced data structures based on balanced and unbalanced tree structures. Special emphasis will be placed on the implementations of these structures and their performance tradeoffs. Both asymptotic complexity analysis and experimental profiling techniques will be introduced. Labs will be used to provide students with hands-on experience in the implementations of various abstract data types and to perform experimental performance analysis. Prerequisite: MATH 210 and EECS 448.
Prerequisite(s): EECS 210 and EECS 448
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Detection of signals in the presence of noise and estimation of signal parameters. Narrowband signals, multiple observations, signal detectability, and sequential detection. Theoretical structure and performance of the receiver.
Prerequisite(s): EECS 861
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Introductory overview of digital communication systems operating over AWGN channels. Topics covered include information sources and source coding, modulation and demodulation, equalization, error control coding, and combined modulation and coding. Students will be required to a number of simulation projects.
Prerequisite(s): EECS 861 or an equivalent graduate level course in random processes
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Discrete-time representation of signals and systems, z-transform properties, signal/system correlation, sampling theory, analysis of linear time-invariant systems, filter implementation, digital filter design, discrete Fourier transform, and the fast Fourier transform
Prerequisite(s): EECS 360
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Adaptive filtering, mathematics for advanced signal processing, cost function optimization, signal processing algorithms for optimum filtering and linear prediction, power spectrum estimation, steepest descent, adaptive algorithms.
Prerequisite(s): EECS 744
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The design of computer systems from the hardware point of view. The implementation of functional and control units and microprogrammed control structures.
Prerequisite(s): EECS 388
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An introduction to digital video for multimedia systems. Topics include basics of digital video, capture and non-linear editing, video feature detection (temporal segmentation, motion estimation), content based video classification, video compression techniques and standards (MPEG-1,2,4,7), video streaming, and multimedia applications. Digital video tools and techniques will be utilized in several programming projects.
Prerequisite(s): EECS 740 or equivalent.
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An introduction to digital video for multimedia systems. Topics include: basics of digital video, capture and non-linear editing, video feature detection (temporal segmentation, motion estimation), content based video classification, video compression techniques and standards (MPEG), video streaming, and multimedia applications. Digital video tools and techniques will be utilized in several programming projects.
Prerequisite(s): EECS 740 or equivalent
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Graduate level directed readings on a topic in electrical engineering, computer engineering, or computer science, mutually agreed-on by the student and instructor. May be repeated for credit on another topic.
Prerequisite(s): Consent of instructor
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Reading under the supervision of an instructor on a topic chosen by the student with the advice of the instructor. May be repeated for additional credit. Consent of the department required for enrollment.
Prerequisite(s): Upper-level EECS eligibility and consent of instructor.
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Mathematical foundations including logic, sets and functions, general proof techniques, mathematical induction, sequences and summations, number theory, basic and advanced counting techniques, solution of recurrence relations, equivalence relations, partial order relations, lattices, graphs and trees, algorithmic complexity and algorithm design and analysis. Throughout there will be an emphasis on the development of general problem solving skills including algorithmic specification of solutions and the use of discrete structures in a variety of applications.
Prerequisite(s): EECS 168 or 169 (or equivalent) and MATH 122
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