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Course Criteria
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3.00 Credits
Geometry of surfaces in 3-dimensional space, including lengths, areas, angles, curvature, and topology. Classification of Euclidean isometries. Classification of compact surfaces having constant Gaussian curvature. Prerequisite(s): Mathematics 235 and 245. Unit(s): 1
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3.00 Credits
Topological properties of the real line and Euclidean space. Convergence, continuity, differentiation, integration properties of real-valued functions of real variables. Prerequisite(s): Mathematics 235 and 250. Mathematics 320 is prerequisite to 321. Unit(s): 1-1
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3.00 Credits
Applications of discrete mathematics from two viewpoints: how mathematical models are used to solve problems from other fields and how problems from other fields stimulate the development of new mathematics. Probabilistic models are emphasized. Examples of problems include analysis of board games, elections, and DNA. Prerequisite(s): Mathematics 245. Unit(s): 1
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3.00 Credits
Continuous models in modern applications. Primary focus on practical understanding of the modeling process, with goals of developing individual modeling skills and ability to critically read modeling reports in scholarly journals. Mathematical topics include ordinary differential and partial differential equations. Prerequisite(s): Mathematics 312. Unit(s): 1
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3.00 Credits
Analysis and implementation of algorithms used in applied mathematics, including root finding, interpolation, approximation of functions, integration, solutions to systems of linear equations. (Same as Computer Science 328.) Prerequisite(s): Mathematics 212 or 232, Mathematics 245, and Computer Science 150 or 155. Unit(s): 1
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3.00 Credits
Introduction to the theory, methods, and applications of randomness and random processes. Probability concepts, independence, random variables, expectation, discrete and continuous probability distributions, moment-generating functions, simulation, joint and conditional probability distributions, sampling theory, laws of large numbers, limit theorems. Prerequisite(s): Mathematics 235. Corequisite: Mathematics 245. Unit(s): 1
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3.00 Credits
Introduction to basic principles and procedures for statistical estimation and model fitting. Parameter estimation, likelihood methods, unbiasedness, sufficiency, confidence regions, Bayesian inference, significance testing, likelihood ratio tests, linear models, methods for categorical data, resampling methods. Prerequisite(s): Mathematics 329. Unit(s): 1
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3.00 Credits
Introduction to the calculus of functions of a single complex variable, including series, calculus of residues, and conformal mapping. Prerequisite(s): Mathematics 310 or Physics 301. Unit(s): 1
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3.00 Credits
Linear and Integer Programming: algorithms, complexity, sensitivity, and duality. Applications such as assignments, networks, scheduling. Prerequisite(s): Mathematics 323. Unit(s): 1
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3.00 Credits
For well-qualified students who wish to work independently in areas not included in curriculum. Proposal must be approved by departmental committee. Prerequisite(s): Permission of department chair and instructor. Unit(s): 0.25-1
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