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Course Criteria
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4.00 Credits
Students implement a compiler for a simplified modern programming language. Theory of compiler construction, including finite-state automata, LL(1) grammars and top-down parsing. Project includes lexical and syntax analysis, name storage, scope and type analysis, error recovery and code generation. Advanced topics covered as time permits, including LR(k) grammars, bottom-up parsing, compiler generators (e.g., LEX and YACC) and code optimization.
Prerequisite:
C- or higher in CSCI 330, 340, 362.
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4.00 Credits
Introduction to artificial intelligence including problem solving, search, heuristic methods, machine learning, knowledge representation, natural language processing, computer vision, expert systems, theorem proving and current applications. Concepts illustrated through programs developed in LISP or Prolog.
Prerequisite:
C- or higher in CSCI 362 and ENGL 110.
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4.00 Credits
An introduction to data mining, including data cleaning, the application of statistical and machine learning techniques to discover patterns in data, and the analysis of the quality and meaning of results. Machine learning topics may include algorithms for discovering association rules, classification, prediction, and clustering. Lab assignments provide practice applying specific techniques and analyzing results. An independent project provides students with the opportunity to guide a project from data selection and cleaning through to presentation of results.
Prerequisite:
C- or higher in CSCI 366 and MATH 235 or 333 or 335.
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4.00 Credits
A practical introduction to data analytics, visualization, and blending theory. Students will learn about and apply various clustering algorithms and techniques for dealing with noisy data, use a distributed data analytics framework, complete laboratory assignments using version control, and enforce reproducibility by having all science easily sharable. Students will become familiar with modern data analytics methods and explore real-world data sets. Visualization of results will be a large component of the course through interactive and static frameworks.
Prerequisite:
C- or higher in CSCI 366 and MATH 235 or 333 or 335.
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4.00 Credits
Intelligent robotic systems that deal with the physical world through visual, acoustic or tactile sensing. Fundamentals of robot vision, including image acquisition and camera geometry, pattern recognition, representation and analysis of shape, pixel neighborhoods, connectivity, distance measures, arithmetic operations on pixels and images, computations of area, centroid, moments, axis of least inertia, correlation techniques, histogram computation, manipulation of robot end effectors, robot task coordination and simple Cartesian robot manipulation.
Prerequisite:
C- or higher in CSCI 362.
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4.00 Credits
Theory and techniques of algorithm design and analysis. For design, students will study a variety of algorithmic solutions to problems from application areas including searching, selecting, sorting, graph theory, number theory and encryption. Design paradigms, including greedy method, divide and conquer, dynamic programming, backtracking and branch-and-bound. For analysis, students will use formal techniques to classify execution time of an algorithm. Software tools are used to measure resources used by a program during execution.
Prerequisite:
C- or higher in CSCI 340.
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4.00 Credits
Provide students with skills and solid technical foundation necessary to design, develop and deploy 3D games and related entertainment technology applications. Topics include 3D game programming, 3D graphics, game design, programming video game controllers, collision detection, force and motion calculations, networking multiplayer games, manipulating sound objects, physical modeling, projectiles, particle systems, physical constraints, deformation of virtual 3D objects, surface deformation, computer animation, forward and inverse kinematics, keyframe motion capture and procedural animation, and behavior-based animation and control.
Prerequisite:
C- or higher in CSCI 362.
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4.00 Credits
Overview of parallel computing through study of parallel programming. Topics include message-passing, highly parallel computations, partitioning and divide-and-conquer strategies, pipelined and synchronous computations, load balancing and termination detection, programming with shared memory systems, parallel sorting algorithms, numerical algorithms, image processing, searching and optimization, and parallel programming using current technology.
Prerequisite:
C- or higher in CSCI 362, 370.
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3.00 Credits
Experimental
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1.00 - 4.00 Credits
Honors Course
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