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Course Criteria
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3.00 Credits
Classification rates, ROC curves, cross-validation techniques, modern regression methods, data reduction/principle components, stages of biomarker development, and study design issues in cancer and occupational research.
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3.00 Credits
The emphasis of this course is to understand the concepts in building both a predictive and a classification model using data mining techniques. Software used for building models is JMP and S-Plus I-Miner. Specific topics include: the distinction between supervised and unsupervised learning; issues in data exploration (steps in data cleaning, missing data, transformations, methods of imputation, training set vs. testing set and validation sets; methods of determining model accuracy (ROC curves, lift and cumulative lift charts); cross-validation, modeling methods for discrete and continuous dependent variables; clustering algorithms; neural networks.
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3.00 Credits
Computational and statistical modeling of human cognitive processes and their implementation: modularity of mind, rule-based vs. distributed vs. prototype models, search techniques, story understanding, and statistical models of language.
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3.00 Credits
Development an application of techniques which allow computation on different components of a network: network technology and design, communications theory, applications protocols, remote procedure calls, distributed storage techniques, parallel algorithms, load balancing, scheduling, and task migration.
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3.00 Credits
Cardinality of sets, uncountability of certain sets, languages, regular languages, context-free languages, the Chomsky hierarchy, Turing machines, Church-Turing thesis, problems that are not Turing computable, and an introduction to computational complexity.
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3.00 Credits
Network, database, and Web security, threat models, elementary and advanced crytology, protocol analysis, covert channels, access control and trust issues, legal and ethical issues in security.
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3.00 Credits
Geometric generation of two- and three-dimensional graphics. Theory of affine transformations. Scan conversion, geometric transformation, clipping, interaction, curves and surfaces, and animation.
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3.00 Credits
No course description available.
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3.00 Credits
Various subjects in computational mathematics. May be repeated for credit when content changes.
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3.00 Credits
Computational analysis of language, with specific reference to techniques for inferring authorship properties. Prerequisites: COSC 160 and MATH 116.
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