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Course Criteria
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3.00 Credits
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended. Prereq., graduate standing or instructor consent.
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3.00 Credits
Focuses on finite difference solution for partial differential equations, methods of SOR, ADI, conjugate gradients, finite element method, nonlinear problems, and applications. Prereqs., CSCI 5606.
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3.00 Credits
Offers direct and iterative solutions of linear systems. Also covers eigen value and eigenvector calculations, error analysis, and reduction by orthogonal transformation. Prereqs., CSCI 5606.
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3.00 Credits
Presents algorithms, simplex, and modifications. Examines theory---duality and complementary slackness. Involves network flow algorithms. Introduces integer programming. Prereq., linear algebra.
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3.00 Credits
Examines systems that span multiple autonomous computers. Topics include system structuring techniques, scalability, heterogeneity, fault tolerance, load sharing, distributed file and information systems, naming, directory services, resource discovery, resource and network management, security, privacy, ethics, and social issues. Recommended prereqs., CSCI 5573 or a course in computer networks. Same as ECEN 5673.
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3.00 Credits
Explores context-free languages: pumping lemma and variants, closure properties, and decision properties. Involves parsing algorithms, including general and special languages, e.g., LR. Additional topics chosen by instructor. Prereq., CSCI 5444 or instructor consent.
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3.00 Credits
Explores algorithms that can extract information about the world from images or sequences of images. Topics covered include: imaging models and camera calibration, early vision (filters, edges, texture, stereo, optical flow), mid-level vision (segmentation, tracking), vision-based control, and object recognition. Recommended prereq., probability, multivariate calculus, and linear algebra.
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3.00 Credits
Same as CSCI 4753.
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1.00 Credits
Class led by a different faculty member of the Institute of Cognitive Science each week. Introduces graduate students to research in cognitive science currently underway within the institute. Prereq., graduate standing or instructor consent.
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3.00 Credits
Provides an advanced treatment of basic database concepts. Prereq., CSCI 2270 and admission as a graduate student in computer science or electrical engineering. Recommended prereqs., CSCI 3287 and 3753.
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