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Graduate Math Courses
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| MATH 231 |
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(CMC, HMC, POM 131)
Principles of Real Analysis I. Countable sets, least upper bounds, and metric space topology including compactness, completeness, connectivity, and uniform convergence. Related topics as time permits. Prerequisite: HMC Prerequisite: Mathematics 12 and 14 (MATH 55 recommended). Pomona Prerequisites; MATH 32 and 60, a proof-based mathematics course above 100 is strongly recommended. |
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| MATH 231S |
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(SCRIPPS 131)
Mathematical Analysis I. By looking carefully at the concept of distance and the notion of an abstract metric space, we will gain a deeper understanding of the Real numbers and of what makes calculus work. Topics will include uncountability, connectedness, and compactness. We will look at continuity in terms of open and closed sets. Offered alternate Fall semesters. |
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| MATH 232 |
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(CMC, HMC, POM 132)
Principles of Real Analysis II. A rigorous study of calculus in Euclidean Spaces including multiple Riemann Integrals, derivatives of transformations, and the inverse function theorem. Prerequisite: Mathematics 131. |
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| MATH 334 |
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(HMC134)
Advanced Complex Analysis. Normal families and the Riemann Mapping Theorem: Weierstrass Product Theorem; harmonic functions; analytic continuation. Additional topics as time permits. Prerequisite: Mathematics 135 or 136, and concurrent enrollment in Mathematics 132. |
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| MATH 235 |
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(CMC 135)
Functions of a Complex Variable. Complex numbers and functions. Complex differentiation. Cauchy-Riemann equations. Holomorphic and harmonic functions. Complex power series, elementary functions. Cauchy's theorem and the deformation theorem. Consequences of Cauchy's theorem: Cauchy's integral formula, Liouville's theorem, fundamental theorem of algebra, Cauchy's formula for derivatives and Morera's theorem. Taylor series, uniqueness and maximum principle. Laurent series, singularities. Residue theorem, calculation of residues. Estimation of integrals. Prerequisite: Mathematics 131 or permission of the instructor. |
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| MATH 236 |
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(HMC 136)
Complex Variables and Applications. Complex differentiation, Cauchy-Riemann equations, Cauchy integral formula, Taylor and Laurent expansions, residue theory, contour integration including branch point contours, uses of Jordan's lemma, Fourier and Laplace transform integrals, conformal mapping. Prerequisite: Mathematics 60 or 64. |
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| MATH 331 |
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(CMC, HMC, POM 137)
Real and Functional Analysis I. Abstract measures, Lebesgue measure on Rn, and Lebesgue-Stieljes measures on R. The Lebesgue integral and limit theorems. Product measures and the Fubini theorem. Additional related topics as time permits. Prerequisite: Mathematics 132. |
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| MATH 332 |
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(CMC, HMC, POM 138)
Real and Functional Analysis II. Continuation of Mathematics 137. Some of the topics covered are: Banach and Hilbert spaces; LP-spaces; complex measures and the Radon-Nikodym theorem. Prerequisite: Mathematics 137 (CGU 331). |
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| MATH 242 |
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(HMC 142)
Differential Geometry. Curves and surfaces, Gaussian curvature, isometries, tensor analysis, covariant differentiation with applications to physics and geometry (intended for physicists and mathematicians). Prerequisite: MATH 64 Pomona Prerequisite MATH 102. (MATH 131 recommended). |
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| MATH 243 |
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(HMC 143)
Topics in Geometry. Selected topics in Riemannian and pseudo-Riemannian geometry, low dimensional manifold theory, contemporary applications in mathematics and physics. Prerequisite: permission of instructor. |
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| MATH 244a |
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(SCRIPPS 144)
Classical and Modern Geometries Do two lines always intersect in exactly one point? We begin with classic Euclidean Geometry, but quickly move to Hyperbolic and Spherical Geometry, where our intuition is challenged. Poincaré model is featured. Next, we use abstract algebra to study projective and finite geometries. Bezout's Theorem leads to Elliptic Curves and modern day research. Prerequisite: MATH 60. |
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| MATH 244 |
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(CMC 144)
Fourier Analysis. |
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| MATH 245 |
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(POM 145)
Topics in Geometry and Topology. Topic varies from year to year and will be chosen from: Differential Topology, Euclidean and Non-Euclidean Geometries, Knot Theory, Algebraic Topology, and Projective Geometry. Prerequisites: Varies from year to year, usually MATH 60 and either a MATH course numbered above 100 or permission of the instructor. Topic for Spring 2005: Differential Topology. Pre-requisite is MATH 131 or permission of the instructor. |
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| MATH 247 |
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(HMC, POM 147)
Topology. Topological spaces, product spaces, quotient spaces, Hausdorff spaces, compactness, connectedness, path connectedness, fundamental groups, homotopy of maps, and covering spaces. Corequisite: Mathematics 131 or permission of instructor. Offered jointly by Harvey Mudd and Pomona Colleges. |
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| MATH 248 |
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(PIT 148)
Knot Theory. An introduction to the theory of knots and links from combinatorial, algebraic, and geometric perspectives. Topics will include knot diagrams,p-colorings, Alexander, Jones, and HOMFLY polynomials, Seifert surfaces, genus, Seifert matrices, the fundamental group, representations of knot groups, covering spaces, surgery on knots, and important families of knots. Prerequisite: Topology (Mathematics 147), or Algebra (Mathematics 171), or permission of instructor. |
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| MATH 449 |
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(HMC 149)
Seminar in Topology. Selected topics from the general area of topology. Prerequisite: Mathematics 147 and permission of the instructor. |
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| MATH 251 |
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(CMC, POM 151)
Probability. Probability spaces, discrete and continuous random variables, conditional and marginal distributions, expectation, independence, generating functions, transformations, central-limit theorem. Prerequisite or Co-requisite for CMC: Mathematics 90 or permission of instructor. Prerequisites for Pomona: MATH 32 or 32H, and 60. |
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| MATH 252 |
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(CMC, HMC, POM 152)
Statistical Theory. Introduction to statistical inference. Sufficiency, estimation of parameters, confidence intervals, and tests of hypotheses. Prerequisite: Mathematics 151 (251) or permission of instructor. [PAC 4] |
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| MATH 253 |
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(CMC 153)
Advanced Topics in Statistical Inference. Selected topics in statistical inference, such as Bayesian inference, bootstrapping, and distribution-free methods. Prerequisite: Mathematics 152. Offered jointly by Claremont Graduate University,Claremont McKenna, and Pomona colleges. Offered in 2004-05. |
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| MATH 256 |
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(CMC, HMC 156)
Stochastic Processes. Continuation of Mathematics 151. Properties of independent and dependent random variables, conditional expectation. Topics chosen from Markov processes, second order processes, stationary processes, ergodic theory, Martingales, and renewal theory. Prerequisite: Mathematics 63 and 151 or permission of instructor. Pomona prerequisite: MATH 151 |
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| MATH 258 |
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(CMC,HMC, POM 158)
Statistical Linear Models. An introduction to analysis of variance (including one-way and two-way fixed effects ANOVA) and linear regression (including simple linear regression, multiple regression, variable selection, stepwise regression and analysis of residual plots). Emphasis will be on both methods and applications to data. Statistical software will be used to analyze data. Prerequisites: Mathematics 58 or 152, or Economics 57, or Psychology 158, or AP Statistics, or permission of instructor. Offered each spring. [PAC 4]. |
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| MATH 259 |
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(POM 159)
Applied Nonparametric Analysis. Covering both traditional and modern techniques in nonparametrics, this course will focus on analyzing data under appropriate assumptions, by investigating the mathematical derivations as well as the computational aspects of various techniques including sign & rank tests, goodness-of-fit tests, Fisher’s exact test, bootstrapping, and permutation tests. Programming skills needed to run these tests will also be developed. Prerequisite: Mathematics 30 or 30H and one of the following: Mathematics 58 or 152, or Economics 57, or Psychology 158, or AP Statistics or Permission of Instructor. [PAC 4] |
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| MATH 264 |
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(HMC 164)
Scientific Computing. (Same as Computer Science 144.) Computational techniques applied to problems in the sciences and engineering. Modeling of physical problems, computer implementation, analysis of results; use of mathematical software; numerical methods chosen from: solutions of linear and nonlinear algebraic equations, solutions of ordinary and partial differential equations, finite elements, linear programming, optimization algorithms, and fast-Fourier transforms. Prerequisites: Mathematics 64, Computer Science 60. 3 credit hours. (Second semester.) |
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| MATH 265 |
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(HMC 165)
Numerical Analysis. An introduction to the theory and methods for numerical solution of mathematical problems. Core topics include: analysis of error and efficiency of methods; solutions of linear systems by Gaussian elimination and iterative methods; calculation of eigenvalue and eigenvectors; interpolation and approximation; numerical integration; solution of ordinary differential equations. Prerequisite: Mathematics MATH 64 and a knowledge of elementary computer programming, or permission of the instructor. Pomona prerequisite: MATH 102. |
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| CS 267 |
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(HMC, POM CS142)
Theory of Computation. Specific topics include finite automata, pushdown automata, Turing machines, and their corresponding languages and grammars; undecidability; and complexity classes, reductions, and hierarchies. Prerequisites: Computer Science 52. |
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| CS 268 |
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HMC 168 (See CS140)
Computer Algorithms. Algorithm design, computer implementation, and analysis of efficiency. Discrete structures, sorting and searching, parsing, pattern-matching, and data management. Reducibility and theoretical limitations. Prerequisite: Computer Science 60 and Mathematics 55. 3 credit hours. (First semester.) |
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| MATH 271 |
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(CMC, HMC, POM 171)
Abstract Algebra I. Groups and isomorphism theorems. Rings and other structures. HMC Prerequisite: Mathematics 55 and MATH 12. Prereq’s for POM – MATH 60; a proof-based math course above 100 is strongly recommended. |
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| MATH 271 |
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(SCRIPPS 171)
Abstract Algebra I. We study some basic structures which appear throughout mathematics including Groups, Rings, and Fields. Topics in group theory will include isomorphism theorems, orbits and stabilizers, and coset partitions. Topics in ring theory will include ideals, quotient rings, and prime and maximal ideals. Ring and field extensions will also be introduced. Prerequisite: Mathematics 60. Offered alternate Spring semesters. |
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| MATH 272 |
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(HMC, POM 172)
Abstract Algebra II. Continuation of MATH 171. Selected topics in the theories of rings, modules, groups, and fields. Typical specific topics include Galois theory of equations and the structure of finitely-generated modules over Euclidean and/or principal ideal domains with applications to linear algebra and finitely-generated Abelian groups. Prerequisite: MATH 171. |
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| MATH 273 |
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(HMC 171)
Advanced Linear Algebra. Topics will be chosen from among: Similarity of matrices and the Jordan form; the Cayley Hamilton Theory, limits of sequences and series of matrices: iterative solutions of large systems of linear algebraic equations; the Perron-Frobenius theory of nonnegative matrices; estimating eigenvalues of matrices. Prerequisites: MATH 131. |
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| MATH 275 |
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(CMC, HMC 175)
Number Theory. Properties of integers, congruences, Diophantine problems, quadratic reciprocity, number theoretic functions, primes. Prerequisite: MATH 55. Pomona prerequisite: MATH 60. |
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| MATH 275S |
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(SCRIPPS 175)
Number Theory. Number Theory is often considered one of the most beautiful and elegant topics in mathematics. We will study properties concerning the integers, such as divisibility, congruences, and prime numbers. More advanced topics include encryption, quadratic reciprocity, and Diophantine approximation. Finally we will introduce elliptic curves and see how these curves relate to the proof of Fermat's last theorem. Prerequisite: MATH 60. Offered alternate Fall semesters. |
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| MATH 277 |
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(POM 177)
Advanced Topics in Algebra. Topic varies from year to year and will be chosen from: Representation Theory, Algebraic Geometry, Commutative Algebra, Algebraic Number Theory, Coding Theory, Algebraic Combinatorics, Algebraic Graph Theory, Matroid Theory. Prerequisite: MATH 171 or permission of instructor. |
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| MATH 280 |
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(HMC 180)
Applied Analysis. Orthogonal series and Sturm-Liouville problems, Fourier series and boundary value problems for partial differential equations, special functions of mathematical physics, integral transforms. Prerequisite: Mathematics 131. (Students may not receive credit for both Mathematics 115 and 180). |
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| MATH 281 |
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(HMC, POM 181)
Dynamical Systems. This course will consider both discrete and continuous dynamics. In any given year it will include most of the following topics: Linear and nonlinear systems; Bifurcation theory, routes to chaos, symbolic dynamics, Sharkovii's theorem and chaos. Existence and uniqueness theory and dependence on data; Hartman-Grobman and Poincaré-Bendixson theorems, Lyapunov stability theory and stable manifold theory. HMC Prerequisite: MATH 115 or 180. Pomona Prerequisite: MATH 102 and 101 or 131. Offered jointly with CGU and HMC. First semester. |
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| MATH 282 |
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(HMC 182, CMC 282)
Partial Differentiation Equations. Theory and applications of quasi-linear and linear equations of first order, including systems, higher order linear and non-linear equations, including classical methods of solutions of the wave, heat and potential equations, Green’s function, similarity solutions, variational techniques, etc. Prerequisite: Mathematics 180, or 115, or permission of the instructor. |
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| MATH 283P |
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(POM 183)
Mathematical Modeling. Introduction to the construction and interpretation of deterministic and stochastic models in the biological, social, and physical sciences, including simulation studies. Students are required to develop a model in an area of their interest. Prerequisite: Mathematics 102. |
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| MATH 284P |
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(POM 184)
Topics in Applied Analysis. Topics will usually be chosen from among: dimension theory; perturbation methods and theory; harmonic analysis and Sturm-Liouville problems; wavelets; diffusion; delay differential equations and integro-differential equations; stability, bifurcation and chaos in dynamical systems; ergodic theory; variational methods; control theory; continuum mechanics and nonlinear elasticity. Prerequisites: MATH 102, and 101 or 131, or permission of instructor. Offered alternate years. |
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| MATH 285 |
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(HMC 185)
Introduction to Wavelets and their Applications. An introduction to the mathematical theory of wavelets, with applications to signal processing, data compression and other areas of science and engineering. Prerequisite: MATH 115 or MATH 180 . |
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| MATH 286 |
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(CMC, HMC, POM 186)
Stochastic Operations Research. Stochastic models of inventory, reliability, queuing, sequencing, and transportation. Applications of these models to problems arising in industry, government, and business. Prerequisite: Mathematics 151(251). |
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| MATH 287 |
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(CMC, HMC, POM 187)
Deterministic Operations Research. Linear, integer, nonlinear and dynamic programming, classical optimization problems, network theory. CGU Prerequisite: Multivariable calculus and linear algebra. |
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| MATH 288H |
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(HMC 188)
Social Choice and Decision-Making. This course focuses on the modeling of individual and group decisions using techniques from game theory. Topics will include: basic concepts of game theory and social choice theory, representations of games, Nash equilibria, utility theory,non-cooperative games, cooperative games, voting games, paradoxes, impossibility theorems, Shapley value, power indices, fair division problems, and applications. This course meets the "Integrative Experience" requirement for Harvey Mudd students. . Prerequisite: MATH 63 and (recommended) MATH 55 or permission of instructor. |
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| MATH 288C |
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(CMC 188)
Game Theory. Games in extensive form, combinatorial games, strategic equilibrium, matrix games and minimax theorem, computation of optimal strategies, co-operative and non-cooperative solutions of bi-matrix games, coalitional games and the core, indices of power, bargaining set, nonatomic games. Prerequisite: Linear algebra (MATH 90). Recommended: Probability (MATH 151) |
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MATH 306
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Optimization |
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The course emphasizes nonlinear programming. It covers numerical methods for finite-dimensional optimization problems with fairly smooth functions. Both non-constrained and constrained optimizations will be discussed. Certain degree of emphasis will be given to the convergence analysis of the numerical methods.
Prerequisite: multivariable caculus and numerical linear algebra. |
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MATH 331
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Real Analysis I |
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Abstract measures, Lebesgue measure on R n , and Lebesgue-Stieljes measures on R. The Lebesgue integral and limit theorems. Product measures and the Fubini theorem. Additional related topics as time permits.
Prerequisite: advanced calculus. Students with no background in general topology are urged to take MATH 247 concurrently. |
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MATH 332
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Real and Functional Analysis II |
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Continuation of MATH 331. Some of the topics covered will be: Banach and Hilbert spaces; L p -spaces; complex measures and the Radon-Nikodym theorem.
Prerequisite: MATH 331 |
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MATH 334
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Complex Analysis II |
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Normal families and the Riemann Mapping Theorem; Weierstrass Product Theorem; harmonic functions; analytic continuation. Additional topics as time permits.
Prerequisites: MATH 236 and corequisite MATH 247 |
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MATH 335
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Integral Transforms and Applications |
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Transforms covered will include: Fourier, Laplace, Hilbert, Hankel, Mellin, Radon, and Z. The course will be relevant to mathematicians and enginners working in communications, signal and image processing, continuous and digital filters, wave propagation in fluids and solids, etc. |
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MATH 336
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Image Processing |
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Prerequisite: B.S. in Computer Science, engineering, math, or physics. Undergraduate probability and statistics and/or linear systems theory. |
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MATH 350
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Data Mining |
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This course provides a broad introduction to the concepts, techniques and algorithms commonly used in data mining. The first half of the course will cover the basic principles of data measurement, exploratory data analysis and visualization, model structures, scoring and evaluation, as well as brief reviews of techniques for classification, regression, and clustering. The remaining half of the course will cover specific application areas in greater depth: some possible topics are Text Mining and Information Extraction, Web data mining, Search Engines, Credit Scoring and Spam Filters (depending on student interests). |
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MATH 351
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Time Series Analysis |
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Analysis of time series data by means of particular models such as ARIMA. Spectral analysis. Associated methods of inference and applications.
Prerequisite: permission of instructor. |
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MATH 352
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Nonparametric Statistical Inference |
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Treatment of statistical questions which do not depend on specific parametric models. Examples are testing for symmetry of a distribution and testing for equality of two distributors. Elementary combinatorial methods will play a major role in the course.
Prerequisite: a statistics course of the level of MATH 158 or 252, or permission of instructor. |
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MATH 353
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Asymptotic Methods in Statistics with Applications |
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Modes of convergence for random variables and their distributions; central limit theorems; laws of large numbers; statistical large smaple theory of functions of sample moments, sample quantiles, rank statistics, and extreme order statistics; asymptotically efficient estimation and hypothesis testing.
Prerequisites: MATH 251 and 252; linear algebra; undergraduate analysis (MATH 131 and 132 or equivalent). |
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MATH 354
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Reliability Theory |
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Structural properties and reliability of complex systems; classes of life distributions based on aging; maintenance and replacement models; fault trees; point and interval estimation techniques for complex systems; accelerated testing; and reliability evaluation plans used by industry and government.
Prerequisite: MATH 158 or 252. |
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MATH 355
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Linear Statistical Analysis |
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A discussion of linear statistical models in both the full and less-than-full rank cases, the Gauss-Markov theorem, and applications to regression analysis, analysis of variance, and analysis of covariance. Topics in design of experiments and multivariate analysis.
Prerequisite: a year course in probability and statistics. |
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MATH 356
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Game Theory |
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Models of conflict and/or cooperation. Equilibrium outcomes for non-cooperative games, and cooperative solution concepts for coalitional games: core theory, stable sets, value theories, the nucleolus, and bargaining sets. Applications to economic markets, voting power, bargaining, join cost allocation.
Prerequisite: graduate standing or permission of instructor. |
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MATH 357
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Advanced Topics and Applications
in Probability Theory |
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Probability spaces, expectation as integration in a probability space, independence, laws of large numbers, central limit theorems, dependent sequences, conditional expectation and probability, Markov and Martingale properties. Applicatioins to the fields of engineering, computer and information science, reliability, statistics, economics and finance, games and gambling, physics, number theory, optimization/numerical analysis, and partial differential equations.
Prerequisites: MATH 251 (probability) and undergraduate analysis through advanced calculus, or with the consent of the instructor. MATH 331 is recommended. |
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MATH 358
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Mathematical Finance |
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This course emphasizes the mathematics used in the valuation of derivative securities. It will cover the necessary tools for modeling price fluctuations in the stock market including Brownian motion, simple stochastic differential equatioins, Ito's lemma, Arbitrage theory, and the Black-Scholes equation. Students will learn how to solve the basic parabolic partial differential equations arising in finance both explicitly and numerically.
Prerequisites: mature understanding of advanced calculus and probability (at the level of MATH 251) and permission of instructor. Some familiarity with simple partial differential equations would be helpful. |
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MATH 362
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Numerical Methods for Partial Differential Equations |
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Finite difference and finite element methods for elliptic, parabolic and hyperbolic partial differential equations. Free and moving boundary problems.
Prerequisites: partial differential equations and numerical analysis. |
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MATH 364
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Introduction to Scientific Computing |
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This 2 unit module is intended to help students develop a basic competence in scientific computing in a PC/workstation environment, thus preparing them for the mathematics clinic and other work in industrial applied mathematics. Students will be given a high level introduction to computing in MATLAB and compiled high-level languages such as C and FORTRAN. A broad collection of basic numerical techniques will be presented including iterative methods for solving nonlinrear equations, approximate integration and differentiation, interpolation, and numerical linear algebra. Additional topics will be covered depending on the interest of the students. By working examples on the computers that illustrate these techniques, students will develop proficiency in the basics of MATLAB and at least one high-level programming language under both Windows and Linux environments. |
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MATH 368
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Advanced Numerical Analysis |
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A course for graduate students and senior level undergraduates. Coverage of selected topics from approximation theory, numerical differentiation and integration, Monte Carlo methods and the numerical solution of differential equations is designed to prepare the student for useful practical work applying numerical methods.
Prerequisites: advanced calculus and elementary numerical analysis. |
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MATH 369
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Monte Carlo & Quasi-Monte Carlo Methods |
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This is an advanced course in which stochastically- motivated mathematical methods are applied to problems of various kinds (e.g. radiation transport, semiconductor, geological and financial modeling, or statistical mechanics) that can be solved by simulations carried out on a computer. Problems studied in this way include the most naturally formulated as integral equations over relatively high dimensional phase spaces, as well as those in which estimates of integrals of functions of a large number of variables are sought. This should be regarded as an advanced course in the applications of probability theory to numerical analysis.
Prerequisites: a graduate course in probability theory, a basic course in numerical methods, and facility in programming a computer using a language such as Fortran, C, or Basic. |
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MATH 374
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Encoding and Encryption |
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The mathematical theory of data encoding and encryption, with much of the necessary abstract algebraic background developed in the course. Topics include: finite groups, rings and fields; residue arithmetic, the Chinese Remainder Theorem, polynomial algebras over finite fields; basic notions of encoding and error correcting capabilities; (n,k)-linear codes with parity check; the Hamming code; cyclic codes; the BCH code; complexity-theoretic foundations of cryptography; one-way and trapdoor functions; secret key and public key encoding; the Data Encryption Standard; the RSA algorithm; the factorization problem; computer implementation of the arithmetic of large numbers; elementary algorithms; the quadratic sieve method; theory of zero-knowledge protocols.
Prerequisites: linear algebra and a substantial course in programming, preferably C++. |
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MATH 377
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Algebra I |
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The Sylow theorems, normal series, and other topics from group theory. Topics from ring theory, including projective and injective models, rings of quotients and localization, chain conditions, primary decomposition of noetherian modules, and the Wedderburn-Artin theorem for semi-simple rings.
Prerequisite: a year course in algebra equivalent to undergraduate MATH 171-172. |
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MATH 378
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Algebra II |
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Topics in algebra selected according to the interests of the instructor and students.
Prerequisite: MATH 377 or permission of instructor. |
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MATH 380
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Topics in Applied Mathematics |
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This course will cover the main components of theoretical fluid mechanics: introduction of the continuum description of a Newtonian fluid, viscous/inviscid flows, boundary-layer theory, compressible/incompressible flows, free-surface hydrodynamics and waves, linear/non-linear acoustics, shock waves.
Prerequisite: undergraduate courses in vector calculus, complex analysis, and ordinary and partial differential equations. |
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MATH 382
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Perturbation and Asymptotic Analysis |
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Asymptotic expansions. Steepest descent methods for integrals, stationary phase. Perturbation methods in ordinary and partial differential equations, including singular perturbations (boundary layers). Averaging and multiple scale technigues. Geometrical optics.
Prerequisite: differential equations |
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MATH 388
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Continuous Mathematical Modeling |
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A course aimed at the construction, simplification, analysis, interpretation and evaluation of mathematical models that shed light on problems arising in the physical and social sciences. Derivation and methods for solution of model equations, heat conduction problems, simple random walk processes, simplification of model equations, dimensional analysis and scaling, perturbation theory, and a discussion of self-contained modular units that illustrate the principle modeling ideas. Students will normally be expected to develop a modeling project as part of the course requirements.
Prerequisite: permission of instructor. |
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MATH 389
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Discrete Mathematical Modeling |
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A companion course to MATH 388 with emphasis on discrete, rather than continuous models. Mathematical topics will normally be drawn from combinatorics, probability, statistics, and operations research.
Prerequisite: permission of instructor. |
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MATH 392-393
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Mathematics Clinic |
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Participation in projects or problems with a substantial mathematical and/or computational content. Students will typically work in teams of 2-4 persons with appropriate faculty supervision. Problems will vary considerably depending on student interest and program of study, but will normally require computer implementation and documentation.
Prerequisite: permission of the faculty |
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