Mathematics (MTH)

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Special contract course requested by Norfolk Schools Portsmouth, for in service teachers to acquire Math Specialist Cerification

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Special Grant Course Requested by Norfolk Public Schools & Va Beach Public School

A graduate-level introduction to advancedintroduction to various graphs, trees, flows innetworks, maps, walks, networks, and cycles. Thiscourse will primarily introduce all the standardgraphs theory results, emphasizing itsapplications in Data Science. Large datasets withmultiple interconnections between datasetvariables can be distilled and illuminated usingvarious graphs, trees, and networks, recognizingsituations where graphs delineate a given dataset.An introduction to the tree search algorithm andsolutions to four color problems is covered.

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Contact the department for specific course information

A graduate level introduction to probability andstatistical with emphasis towards applications indata sciences. Probabilistic and statisticalmethods regularly provide the foundations for datascience, the methodologies included in this coursewill provide the students the knowledge needed inseveral fields as marketing, finance, and otherdisciplines. This course will prepare the studentsfor modeling and understanding big data problems.

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A graduate level introduction to mathematicalfoundations for machine learning provides acollection of tools for doing machine learning.While the theory of the tools may be technical,the emphasis is on a balance between theory andpractice, with hands-on activities assigned tohelp the understanding of the theory.

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This course is a continuation of linear algebra,towards topics relevant to applications as well astheoretical concepts. The course starts with areview of matrices, linear systems, subspaces,determinants, eigenvalues and eigenvectors, andorthogonal vectors. Then it introduces the basictechniques, analysis methods, and implementationdetails of numerical linear algebra. Emphasiswill be given on the matrix computations thatarise in solving linear systems, least squaresproblems, and eigenvalue problems. Students willdemonstrate knowledge by completing a finalproject that demonstrates understanding of linearsystems applications.

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A graduate level introduction to numericalalgorithms for linear algebra problems withapplications to data analytics. Algorithms will bestudied and analyzed for efficiency and accuracy.Topics include Singular Value Decomposition, QRfactorization, Least Squares, Conditioning andStability, Systems of Equations, Eigenvalues andEigenvalue algorithms and Iterative methods.

This course presents a graduate levelcomprehensive introduction to data visualizationand technical reporting. The course will providethe students with the necessary background forvisual representation and analytics of complexdata and data communication to a target audience.The course will cover design strategies,techniques to display multidimensional informationstructures, and exploratory visualization tools.As part of the course, students will be requiredto present written reports and oral presentations.

A graduate level introduction to new techniquesfor predictive descriptive learning using conceptsfrom statistics, programming and artificialintelligence with emphasis on statistical aspectsand integration with standard methodologies.Course covers regression and classification modelswith descriptive methods to discover patterns anddata relationships without inference. This coursewill prepare students to view data from astatistical perspective with automated analysis oflarge complex data sets.

The course structure follows a graduate case-studymodel. Throughout the semester, students will bepresented with various case studies ofmathematical models as applied to the fields ofengineering, technology, natural/physical science,social science, business, and/or management. Completion of a formal project with proposaldescribing the modeling problem with outline of apossible solution path concluding in guidedsolution as primary focus. Regular progressreports and presentation of the completed projectby the end of the semester will be required. Theproject will provide solution(s) to the modelingproblem and demonstrate skill on problem-solving,data-fitting, writing, and presenting.

A graduate level of statistical methods withemphasis towards applications in data sciences.Statistical learning methods regularly provide thefoundations for data science, the methodologiesincluded in this course will provide the studentsthe knowledge needed in several fields asmarketing, finance, and other disciplines. Thiscourse will prepare the students for modeling andunderstanding the fundamentals of statisticalmethods useful for modeling, analyzing andforecasting problems, which include big data.

A graduate level introduction to issues of ethicaldeliberation involved data analytics includingtopics like machine learning and working withincomplete data. Issues on how to collect data toreflect population of interest, model validationswith appropriate error rate, model performance tostandards when deployed are explored. Choice oflearning algorithm and approach to maximizemodels' performance with interpretability withconsideration of ethics into trade-offconsiderations are studied. D ecision making forreal-world effects. Reporting and communicationtopics emphasized through projects.