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Course Criteria
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3.00 Credits
Description: Introduction to the principles of computer security. Discussion of threats, intrusion, trust,?protection,?access control and cryptography and implementation of security, confidentiality and integrity policies. Prerequisites & Notes: Prerequisite, CS 312. Credits: (4) Repeatable for Credit No Grading Basis Letter
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3.00 Credits
Description: Graphic I/O devices; two-dimensional and three-dimensional display techniques; display processors; clipping and windowing; hidden line removal; data structures for graphics. Prerequisites & Notes: Prerequisite, CS 302 and CS 325. Credits: (4) Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: Advanced graphics in 3-D with vector tools. Topics include: transformations, affine transformations, changing coordinate system, drawing scenes, modeling shapes, solid modeling, and smooth objects. Prerequisites & Notes: Prerequisite, CS 440. Credits: (4) Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: Computer vision includes image acquisition, preprocessing, segmentation (thresholding, edge- and region-based segmentation), shape representation, object recognition, motion analysis, object tracking and 3-D scene reconstruction. Prerequisites & Notes: Prerequisite, CS 302. Credits: (4) Consent No Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: The relationship of user interface design to human-computer interaction. Types of user interfaces, methods of evaluation, user centered design and task analysis, programming tools and environments, and hardware devices. Prerequisites & Notes: Prerequisites, CS 301, CS 325, and MATH 311 or BUS 221. Credits: (4) Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: Device protocols; network configurations; encryption; data compression and security; satellite networks. Prerequisites & Notes: Prerequisites, CS 301, CS 311, and CS 325. Credits: (4) Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: Introduction to the principles of artificial intelligence. Pattern matching, knowledge representation, natural language processing, expert systems. Prerequisites & Notes: Prerequisites, CS 302, CS 325, CS 362 and MATH 330. Credits: (4) Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: Data mining methods for discovering hidden patterns in large databases and data warehouses with applications in business, science, and engineering. Prerequisites & Notes: Prerequisites, CS 420 or permission of instructor and MATH 311 or BUS 221. Credits: (4) Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: Introducing concepts, models, algorithms, and tools for development of intelligent systems: artificial neural networks, genetic algorithms, fuzzy systems, swarm intelligence, and hybridizations of these techniques. Prerequisites & Notes: Prerequisites, CS 302, CS 325, CS 362, and MATH 330. Credits: (4) Repeatable for Credit No Grading Basis GRD
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3.00 Credits
Description: Implementation of a significant project relating to artificial intelligence. Prerequisites & Notes: Prerequisite, CS 325 and CS 455. Credits: (2) Repeatable for Credit No Grading Basis GRD
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