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Course Criteria
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3.00 Credits
Point-set theory, compactness, completeness, connectedness, total boundedness, density, category, uniform continuity and convergence, Stone-Weierstrass theorem, fixed point theorems. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
Graduate level introduction to probabilistic methods, including linearity of expectation, the deletion method, the second moment method and the Lovasz Local Lemma. Many examples from classical results and recent research in combinatorics will be included throughout, including from Ramsey Theory, random graphs, coding theory and number theory. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
Theory of limiting distributions; interval and point estimation, sufficient statistics. Bayesian procedures, hypothesis testing, nonparametric methods. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
Linear regression and correlation models, regression parameters, prediction and confidence intervals, time series, analysis of variance and covariance. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
In addition to the theoretical constructs in financial mathematics, there are also a range of computational/simulation techniques that allow for the numerical evaluation of a wide range of financial securities. This course will introduce the student to some such simulation techniques, known as Monte Carlo methods, with focus on applications in financial risk management. Monte Carlo and Quasi Monte Carlo techniques are computational sampling methods which track the behavior of the underlying securities in an option or portfolio and determine the derivative's value by taking the expected value of the discounted payoffs at maturity. Recent developments with parallel programming techniques and computer clusters have made these methods widespread in the finance industry. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
Random vectors, sample geometry and random sampling, generalized variance, multivariate normal and Wishart distributions, estimation of mean vector, confidence region, Hotelling's TU, covariance, principal components, factor analysis, discrimination, clustering. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
Various type of designs for laboratory and computer experiments, including fractional factorial designs, optimal designs and space filling designs. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
Categorical data analysis, contingency tables, log-linear models, nonparametric methods, sampling techniques. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
The wealth of observational and experimental data available provides great opportunities for us to learn more about our world. This course teaches modern statistical methods for learning from data, such as regression, classification, kernel methods, and support vector machines. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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3.00 Credits
Fundamentals of matrix theory; least squares problems; computer arithmetic, conditioning and stability; direct and iterative methods for linear systems; eigenvalue problems. Credit may not be granted for both Math 577 and Math 477. Prerequisite: An undergraduate numerical course, such as MATH 350 or instructor permission. 3. 000 Credit Hours 3. 000 Lecture hours Levels: Graduate Doctoral, Graduate Business, Graduate, Undergraduate Schedule Types: Lecture College of Science & Letters College Applied Mathematics Department
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