In this course, we will discuss stochastic modeling, algorithms, and mathematical and statistical analysis for uncertainty quantification in complex systems. Topics to be covered include: stochastic modeling and related numerical methods, parameter selection and estimation, Bayesian methods for parameter estimation and inverse problems, stochastic spectral methods, surrogate models, sensitivity analysis.

Advanced programming techniques in the Java language. Elementary techniques of software engineering: documentation, coding style, basic testing principles, and informal reasoning about correctness. The notion of an abstract data structure and various important data structures: stacks, queues, linked lists, tree-based structures, and hash tables. Use of object-oriented language constructs for encapsulation of data objects. Lecture, programming projects.

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