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CSCE 632: Expert Systems
3.00 Credits
Texas A & M University-Galveston
Expert Systems. (3-0). Credit 3. Basic concepts for building expert systems; inference strategies; applications and case studies; techniques for knowledge acquisition; use of existing tools for building expert systems. Prerequisite: CSCE 420 or 625.
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CSCE 633: Machine Learning
3.00 Credits
Texas A & M University-Galveston
Machine Learning. (3-0). Credit 3. Machine learning is the study of self-modifying computer systems that can acquire new knowledge and improve their own performance; survey machine learning techniques, which include induction from examples, conceptual clustering, explanation-based learning, exemplar learning and analogy, discovery and genetic algorithms. Prerequisite: CSCE 420 or 625.
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CSCE 633 - Machine Learning
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CSCE 634: Intell User Interface
3.00 Credits
Texas A & M University-Galveston
Intelligent User Interfaces. (3-0). Credit 3. Intersection of artificial intelligence and computer-human interaction: emphasis on designing and evaluating systems that learn about and adapt to their users, tasks, and environments. Prerequisites: Graduate classification and approval of instructor.
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CSCE 636: Neural Networks
3.00 Credits
Texas A & M University-Galveston
Neural Networks. (3-0). Credit 3. Basic concepts in neural computing; functional equivalence and convergence properties of neural network models; associative memory models; associative, competitive and adaptive resonance models of adaptation and learning; selective applications of neural networks to vision, speech, motor control and planning; neural network modeling environments. Prerequisites: MATH 304 and 308 or approval of instructor.
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CSCE 637: Complexity Theory
3.00 Credits
Texas A & M University-Galveston
Complexity Theory. (3-0). Credit 3. Deterministic, non-deterministic, alternating and probabilistic computations; reducibilities; P, NP and other complexity classes; abstract complexity; time, space and parallel complexity; and relativized computation. Prerequisite: CSCE 627 or approval of instructor.
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CSCE 639: Fuzzy Logic & Intl Sys
3.00 Credits
Texas A & M University-Galveston
Fuzzy Logic and Intelligent Systems. (3-0). Credit 3. Introduces the basics of fuzzy logic and its role in developing intelligent systems; topics include fuzzy set theory, fuzzy rule inference, fuzzy logic in control, fuzzy pattern recognition, neural fuzzy systems and fuzzy model identification using genetic algorithms. Prerequisite: CSCE 625 or approval of instructor. Cross-listed with MEEN 676.
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CSCE 640: Quantum Algorithms
3.00 Credits
Texas A & M University-Galveston
Quantum Algorithms. (3-0). Credit 3. Introduction to the design and analysis of quantum algorithms; basic principles of the quantum circuit model; gives a gentle introduction to basic quantum algorithms; reviews recent results in quantum information processing. Prerequisite: CSCE 629 or approval of instructor.
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CSCE 641: Computer Graphics
3.00 Credits
Texas A & M University-Galveston
Computer Graphics. (3-0). Credit 3. Representations of 3-dimensional objects, including polyhedral objects, curved surfaces, volumetric representations and CSG models; techniques for hidden surface/edge removal and volume rendering; illumination and shading; anti-aliasing; ray tracing; radiosity; animation; practical experience with state-of-the-art graphics hardware and software. Prerequisite: CSCE 441. Cross-listed with VIZA 672.
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CSCE 643: Sem Intel Sys Robotics
3.00 Credits
Texas A & M University-Galveston
Seminar in Intelligent Systems and Robotics. (3-0). Credit 3. Problems, methods and recent developments in intelligent systems and robotics. This course may be taken at multiple times for credit as content varies. Prerequisite: Approval of instructor.
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CSCE 644: Cortical Networks
3.00 Credits
Texas A & M University-Galveston
Cortical Networks. (3-0). Credit 3. The architecture of the mammalian cerebral cortex; its modular organization and its network for distributed and parallel processing; cortical networks in perception and memory; neuronal microstructure and dynamical simulation of cortical networks; the cortical network as a proven paradigm for the design of cognitive machines. Prerequisites: CSCE 420 or CSCE 625 and 636 and graduate classification.
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CSCE 644 - Cortical Networks
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