Mathematics (primarily algebra) preparatory to MATH 101. Topics include: solving linear equations, inequalities, and system; solving quadratic, radical, and rational equations; and introduction to imaginary numbers. Prerequisite: Two years of high school college preparatory mathematics, algebra and geometry, and a score of 16 or more on ACT mathematics; or a qualifying score on the mathematics placement test. MATH 002 is the lowest level mathematics course offered at the University of Kansas, and does not count towards the 120 credit hours required for graduation. Students not prepared for MATH 101 will be permitted to enroll in MATH 002. However, before enrolling in MATH 002, such students are encouraged to prepare by self-study or by completing a beginning algebra course in high school, community college, or correspondence study.

The course comes in the three following forms based on the instructional approach to support different student groups. Standard: The regular version of college algebra. With Practice Workshops: Meets 5 days per week, 4-credit (co-enrollment in the corresponding section of MATH 197 is required). Department permission required to enroll. For students who can benefit from additional review during the course. Students with slightly lower ACT scores may be admitted based on high school GPA. Also recommended for students passing Math 002 with a lower grade. Online: Intended for students who are self-motivated, studying abroad, or who have irregular schedules or alternate class needs. Features: self-driven material, in-person exams. All versions share the same material: coordinate systems, functions and their graphs; linear, quadratic, general polynomial, rational, exponential, and logarithmic functions; equations and inequalities; and linear and non-linear systems. Not open to students with credit in MATH 104. Prerequisite: MATH 002, or two years of high school algebra and a score of 22 or higher on ACT mathematics, or a qualifying score on the mathematics placement test. Students with slightly lower ACT scores may be admitted to With Practice Workshops sections based on high school GPA.

The circular functions and their applications. Not open to students with credit in MATH 104. May not be used to fulfill the College mathematics requirement. Prerequisite: MATH 101, or two years of high school algebra and a score of 26 or higher on enhanced ACT mathematics, or a qualifying score on the mathematics placement test.

An introduction to the elementary functions (polynomial, rational, exponential, logarithmic, and trigonometric) and their properties. Open for only two hours credit for students with credit in MATH 101. Not open to students with credit in MATH 103. Prerequisite: MATH 002, or two years of high school algebra and a score of 22 or higher on ACT mathematics, or a qualifying score on the mathematics placement test.

This diverse course introduces students to foundational quantitative reasoning skills that will assist them throughout their college-level work and beyond. Topics may include logic and problem solving, personal finance, elementary statistics and data analysis, voting theory and fair division problems, basic linear programming, and network theory. Students taking this class will gain an appreciation for how mathematical thinking can be used in everyday decision making. Prerequisite: MATH 101 or MATH 104, or two years of high school algebra and a score of 26 or higher on ACT mathematics, or a qualifying score on the mathematics placement test.

This course is designed to give the prospective elementary school teacher an overview of several components of the elementary school mathematics curriculum, including number systems, estimation, inequalities and order, sequences and patterns, sets, and relations and functions. The class meets each week for three one-hour instruction sessions and one two-hour laboratory session. This course may not be used to satisfy the College mathematics requirement. Prerequisite: MATH 101 or equivalent placement.

Continuation of MATH 109, including geometry (including transformations) and elementary probability and statistics. Class meets each week for three one-hour instruction sessions and one two-hour laboratory session. This course does not serve as a prerequisite for any mathematics course. It may not be used to satisfy the College mathematics requirement. Prerequisite: MATH 109.

Elementary differential and integral calculus, with applications in management and the biological sciences. Not open to students with credit in MATH 125 or MATH 145. Prerequisite: MATH 101 or MATH 104, or two years of high school algebra and a score of 26 or higher on ACT mathematics, or a qualifying score on the mathematics placement test.

Continuation of MATH 115 including exponential, logarithmic, and trigonometric functions, techniques of integration, and the calculus of functions of several variables. Not open to students with credit in MATH 127 or MATH 147. Prerequisite: MATH 115 plus a course in trigonometry, or MATH 125 or MATH 145. MATH 103 may be taken concurrently.

Limits, continuity and derivatives of algebraic, trigonometric, exponential and logarithmic functions. Curve sketching, optimization and other applications of the derivative. Antiderivatives, Riemann sums, the definite integral, and the fundamental theorem of calculus. Open for only 1 hour credit to students with credit in MATH 115. Not open for credit to students with credit in MATH 116 or MATH 145. Prerequisite: MATH 103 or MATH 104, with a grade of C- or higher; or 3 years of college preparatory mathematics including trigonometry, with a score of 28 or higher on the ACT Mathematics exam, or a qualifying score on the mathematics placement test.

Techniques of integration, including integration by parts. Applications of integration, including volume, arc length, work and average value. Infinite sequences and series and Taylor series. Polar coordinates, vectors and the geometry of space. Open for only 2 hours of credit to students with credit in MATH 116. Not open for credit to students with credit in MATH 146. Prerequisite: MATH 116, MATH 125, or MATH 145, with a grade of C- or higher.

Multivariable functions, partial derivatives and their applications, multiple integrals and their applications. Vector-valued functions, line and surface integrals, Green, Gauss and Stokes Theorems. Not open for credit to students with credit in MATH 147. Prerequisite: MATH 126 or MATH 146, with a grade of C- or higher.

Limits, continuity and derivatives of algebraic, trigonometric, exponential and logarithmic functions. Curve sketching, optimization and other applications of the derivative. Antiderivatives, Riemann sums, the definite integral, and the fundamental theorem of calculus. Open for only 1 hour credit to students with credit in MATH 115. Not open for credit to students with credit in MATH 116 or MATH 125. Prerequisite: An ACT Math score of 34 or higher, or membership in the University Honors Program and an ACT Math score of 32 or higher.

Techniques of integration, including integration by parts. Applications of integration, including volume, arc length, work and average value. Infinite sequences and series and Taylor series. Polar coordinates, vectors and the geometry of space. Open for only 2 hours credit to students with credit in MATH 116. Not open for credit to students with credit in MATH 126. Prerequisite: MATH 125, or MATH 145, with a grade of C- or higher; and invitation of the Department of Mathematics.

Multivariable functions, partial derivatives and their applications, multiple integrals and their applications. Vector-valued functions, line and surface integrals, Green, Gauss and Stokes Theorems. Not open for credit to students with credit in MATH 127. Prerequisite: MATH 126 or MATH 146, with a grade of C- or higher; and invitation of the Department of Mathematics.

Offered to provide opportunities for deeper understanding of freshman-sophomore mathematics through interactive learning. Topics will vary. May be repeated for additional credit. Prerequisite: Variable.

Study of the use of functions in mathematical modeling, with topics drawn from algebra, analytic geometry, statistics, trigonometry, and calculus. These topics include function properties and patterns, complex numbers, parametric and polar equations, vectors and various growth models. The course also includes inquiry methods, collaborative problem solving, the use of multiple representations and data analysis techniques, and the justification and presentation of results. Central to the course are investigative labs employing various technologies and software. The course is designed to help prepare students for secondary school mathematics teaching. (Same as PHSX 209.) Prerequisite: MATH 126 or MATH 146.

Linear ordinary differential equations, Laplace transforms, systems of equations, and applications. Not open to those who have taken MATH 320. Prerequisite: MATH 126 or MATH 146 with grade of C- or higher; previous or concurrent enrollment in MATH 290 or MATH 291 recommended.

Linear Ordinary Differential Equations, Laplace Transforms, Systems of Equations, Enrichment Applications. Prerequisite: MATH 126 or MATH 146 with grade of C- or higher, and invitation from the Department of Mathematics; previous or concurrent enrollment in MATH 290 or MATH 291 recommended. Not open to students with credit in MATH 320.

Systems of linear equations, matrices, vector spaces, linear transformations, and applications. Not open to those who have taken MATH 590. Prerequisite: MATH 126 or MATH 146 with grade of C- or higher.

Systems of Linear Equations, Matrices, Vector Spaces, Linear Transformations, Enrichment Applications. Prerequisite: MATH 126 or MATH 146 with a grade of C- or higher, and invitation from the Department of Mathematics. Not open to students who have taken MATH 590.

Designed for the study of special topics in mathematics at the freshman/sophomore level. May be repeated for additional credit; does not count toward the major or minor in mathematics. Prerequisite: Variable.

Linear ordinary differential equations, series solutions. Laplace transforms. Systems of equations. Not open to those who have taken MATH 220. Prerequisite: MATH 127 or MATH 147 with a grade of C- or higher, and MATH 290 or MATH 291.

This course will cover elementary descriptive statistics of a sample of measurements; probability; the binomial, Poisson, and normal distributions, populations and sampling from populations; and simple problems of statistical inference. May not be counted for junior-senior credit toward a major in mathematics. Not open to students with credit in DSCI 202, BIOL 370, MATH 465, MATH 526, or MATH 628. Prerequisite: MATH 101, MATH 104, or two years of high school algebra and a score of 26 or higher on ACT mathematics, or a qualifying score on the mathematics placement test.

Study of selected topics from Euclidean, non-Euclidean, and transformation geometry chosen to give breadth to the mathematical background of secondary and middle school teachers. May not be counted for junior-senior credit towards a major in mathematics. Prerequisite: MATH 126 or MATH 146. Students enrolled in MATH 409 must concurrently enroll in MATH 410.

Study of selected topics from mathematical history chosen to provide students with knowledge of major historical developments in mathematics including individual contributions and contributions from different cultures. These topics will include a historical development of Euclidean and non-Euclidean geometry. May not be counted for junior-senior credit towards a major in mathematics. Prerequisite: MATH 126 or MATH 146. Students enrolled in MATH 410 must concurrently enroll in MATH 409.

Basic topics in discrete mathematics including sets, logic, relations and functions, graphs and combinatorics. Advanced topics chosen from partially ordered sets and lattices, Boolean algebras, automata, game theory, coding theory, cryptography, optimization and enumeration. Prerequisite: MATH 290.

A careful formulation of convergence and limits of sequences and functions; continuity and properties of continuous functions; differentiation; the Riemann integral; mean-value theorems and the fundamental theorem of calculus. Not open to students with credit in MATH 765. Prerequisite: MATH 127 or MATH 147, and MATH 290 or MATH 291.

Finite state automata and regular expressions. Context-free grammars and pushdown automata. Turing machines. Models of computable functions and undecidable problems. The course emphasis is on the theory of computability, especially on showing limits of computation. (Same as EECS 510.) Prerequisite: EECS 210 and upper-level EECS eligibility.

A first course in statistics for students with the techniques of calculus at their disposal. The following topics are studied with illustrations and problems drawn from various fields of applications: basic notions of probability and probability distributions; classical estimation and testing procedures for one and two sample problems; chi-square test. Not open to those with credit in MATH 628. Prerequisite: MATH 127 or MATH 147 or MATH 116 (MATH 127 or MATH 147 recommended.)

Divisibility, primes and their distribution, the Euclidean algorithm, perfect numbers, Fermat's theorem, Diophantine equations, applications to cryptography. Prerequisite: MATH 127 or MATH 147.

Development of the number systems. Polynomials. Introduction to abstract number systems such as groups and fields. Not open to students with credit in MATH 791. Prerequisite: MATH 290 or MATH 291.

Selected topics in Euclidean geometry. Synthetic and analytic projective geometry; duality, Desargues' theorem, perspectives, conics. Non-Euclidean and metric projective geometries. Prerequisite: MATH 127 or MATH 147.

An introduction to numerical methods and their application to engineering and science problems. Applied treatment of elementary algorithms selected from the subject areas: finding roots of a single nonlinear equation, numerical differentiation and integration, numerical solution of ordinary differential equations. Emphasis on implementing numerical algorithms using the computer. Not open to students with credit in MATH 781 or MATH 782. Prerequisite: MATH 220 and MATH 290, or MATH 320.

This course provides an introduction to topics in data science and machine learning with an emphasis on computation and applications. Programming for the course uses the student's choice of Matlab, Python, or R. Topics covered include dimension reduction, regression techniques, density estimation, machine learning, data assimilation, and clustering and classification techniques. Prerequisite: MATH 290 or equivalent.

Vector spaces, linear transformations, and matrices. Canonical forms, Determinants. Hermitian, unitary and normal transformations. Not open to students with credit in MATH 790. Prerequisite: MATH 127 or MATH 147, and MATH 290 or MATH 291.

An introduction to numerical linear algebra. Possible topics include: applied canonical forms, matrix factorizations, perturbation theory, systems of linear equations, linear least squares, singular value decomposition, algebraic eigenvalue problems, matrix functions, and the use of computational software. Not open to students with credit in MATH 782. Prerequisite: MATH 290 or MATH 291. EECS 138 or equivalent recommended.

Arranged as needed to present appropriate material to groups of students. May be repeated for additional credit. Prerequisite: Variable.

Topics motivated by applications in computer science, studied from a mathematical perspective, and based on methods from linear and abstract algebra. Examples of topics include error-correcting codes, cryptography, and computer algebra. May be repeated with different topics for additional credit. Prerequisite: MATH 290 or MATH 291.

This course provides an introduction to regression analysis and statistical learning with an emphasis on mathematical understanding and its software implementation. Programming uses Python, R, or Julia. Covered topics include the following. Linear regression: parameter estimation, confidence ellipsoids and prediction intervals, hypothesis tests. Classification: logistic regression, linear discriminant analysis. Basis expansion: polynomial regression, regression splines. Resampling methods: cross-validation, bootstrap. Shrinkage methods. Model selection: information criteria, forward and backward selection, lasso. Decision trees and random forests: bagging, boosting. Prerequisite: MATH 290 or MATH 291, and MATH 526 or MATH 628.

This course provides an introduction to main statistical concepts in data science with an emphasis on mathematical understanding and its software implementation. Programming uses Python or Julia. Covered statistical models include linear regression and linear classification for high-dimensional problems; support vector machines and flexible discriminants; Bayesian learning and the EM algorithm; Monte Carlo methods; probabilistic graphical models; unsupervised learning. Prerequisite: A calculus-based statistics course (MATH 628 or MATH 526) and a linear algebra course (MATH 290 or MATH 291). Recommended: EECS 138 or equivalent experience.

This course provides an introduction to time series analysis with an emphasis on mathematical understanding and its software implementation. Programming uses Python, R, or Julia. Covered topics include the following. Modeling time series, trend, seasonality and residual process. Autocovariance function, multivariate time series, moving average and autoregression. Stationary processes, linear processes, linear filtering. Confidence intervals for the mean and the autocorrelation, hypothesis tests for a time series model. ARMA models, partial autocorrelation function, parameter estimation methods, forecasting, model selection. Stationary processes in the frequency domain, spectral density, periodogram, smoothing, spectral window. Nonstationary time series, ARIMA models. State-space representation, Kalman recursions. Recurrent neural networks as time allows. Prerequisite: MATH 290 or MATH 291, and MATH 526 or MATH 628.

Theory and applications of discrete probability models. Elementary combinatory analysis, random walks, urn models, occupancy problems, and the binomial and Poisson distributions. Prerequisite: MATH 127 or MATH 147, and MATH 290 or MATH 291.

Introduction to mathematical probability; combinatorial analysis; the binomial, Poisson, and normal distributions; limit theorems; laws of large numbers. Prerequisite: MATH 127 or MATH 147 and MATH 290 or MATH 291.

An introduction to sampling theory and statistical inference; special distributions; and other topics. Prerequisite: MATH 627.

This course is an introduction to some of the notions and computations in actuarial mathematics. Many computations are associated with compound interest with applications to bank accounts, mortgages, pensions, bonds, and annuities. Life contingencies are considered for annuities and insurance. Some introduction to option pricing is given, particularly the Black-Scholes formula. This course provides the background material needed for some of the initial examinations given by the societies for actuaries, including the Financial Mathematics Exam. Prerequisite: MATH 526 or MATH 627 or a comparable course in probability.

Analytic functions of a complex variable, infinite series in the complex plane, theory of residues, conformal mapping and applications. Prerequisite: MATH 127 or MATH 147.

Boundary value problems; topics on partial differentiation; theory of characteristic curves; partial differential equations of mathematical physics. Prerequisite: MATH 127 or MATH 147 and MATH 220 or MATH 221 or MATH 320.

Topics in the calculus of variations, integral equations, and applications. Prerequisite: MATH 127 or MATH 147 and MATH 220 or MATH 221 or MATH 320.

This course provides an introduction to nonlinear ordinary differential equations and dynamical systems theory with an emphasis on applications. Topics covered include the existence and uniqueness of solutions to initial value problems, as well as the qualitative behavior of solutions, including existence of equilibria, periodic and connecting orbits and their stability. Additional topics include an introduction to bifurcation theory and chaos. Prerequisite: MATH 127 or MATH 147, and MATH 220 or MATH 221 or MATH 320, and MATH 290 or MATH 291.

An introduction to modern geometry. Differential geometry of curves and surfaces, the topological classification of closed surfaces, dynamical systems, and knots and their polynomials. Other topics as time permits. Prerequisite: MATH 127 or MATH 147 and MATH 290 or MATH 291.

Continuation of MATH 660. Prerequisite: MATH 660 or permission of instructor.

Arranged as needed to present appropriate material to groups of students. May be repeated for additional credit. Prerequisite: Variable.

Directed reading on a topic chosen by the student with the advice of an instructor. May be repeated for additional credit. Consent of the department required for enrollment.

An introduction to enumerative combinatorics. Topics include basic counting principles, induction and recursion, graph theory, partitions and compositions, generating functions, inclusion/exclusion, and Polya-Redfield theory. Prerequisite: MATH 290 or MATH 291 and a MATH course numbered 450 or higher.

Graphs; trees; connectivity; Menger's theorem; eulerian and hamiltonian graphs; planarity; coloring of graphs; factorization of graphs; matching theory; alternating chain methods; introduction to matroids with applications to graph theory. Prerequisite: MATH 290 and a math course numbered 450 or higher.

A mathematical introduction to premeasure-theoretic probability. Topics include probability spaces, conditional probabilities and independent events, random variables and probability distributions, special discrete and continuous distributions with emphasis on parametric families used in applications, the distribution problem for functions of random variables, sequences of independent random variables, laws of large numbers, and the central limit theorem. Prerequisite: MATH 290, or equivalent.

Theory of point estimation and hypothesis testing with applications. Confidence region methodologies and relations to estimation and testing. Prerequisite: MATH 727 or equivalent.

Stochastic adaptive control methods. Stochastic processes such as Markov chains and Brownian motion, stochastic integral, differential rule, stochastic differential equations, martingales and estimation techniques. Identification and control of discrete and continuous time linear stochastic systems. Specific applications and simulation results of stochastic adaptive control theory. Prerequisite: MATH 627 and some knowledge of control.

MATH 765 and MATH 766 are theoretical courses on the fundamental concepts of analysis and the methods of proof. These two courses include the concept of a real number; limits, continuity, and uniform convergence; derivatives and integrals of functions of one and of several real variables. Prerequisite: MATH 290, or equivalent.

A continuation of MATH 765. Prerequisite: MATH 765.

Finite and divided differences. Interpolation, numerical differentiation, and integration. Gaussian quadrature. Numerical integration of ordinary differential equations. Curve fitting. (Same as EECS 781.) Prerequisite: MATH 320 and knowledge of a programming language.

Direct and interactive methods for solving systems of linear equations. Numerical solution of partial differential equations. Numerical determination of eigenvectors and eigenvalues. Solution of nonlinear equations. (Same as EECS 782.) Prerequisite: EECS 781 or MATH 781.

Finite difference methods applied to particular initial-value problems (both parabolic and hyperbolic), to illustrate the concepts of convergence and stability and to provide a background for treating more complicated problems arising in engineering and physics. Finite difference methods for elliptic boundary-value problems, with a discussion of convergence and methods for solving the resulting algebraic system. Variational methods for elliptic problems. Prerequisite: MATH 647 or equivalent.

A theoretical course on the fundamental concepts of linear algebra. Topics to be covered include: vector spaces, bases, dimension, subspaces, dual spaces; inner product spaces; linear operators on finite dimensional inner product spaces, including self-adjoint and normal operators; spectral theorems over the real and complex numbers; various canonical forms; singular values theorem. Prerequisite: MATH 590 or its equivalent.

Topics in the theory of groups, rings and fields at the graduate level, including: normal subgroups, isomorphism theorems, examples of finite groups, symmetric groups, class equation, applications of Sylow theorems, integral domains, PIDs and UFDs, algebraic extensions of fields, rudiments of Galois theory. Prerequisite: MATH 558 or consent of the instructor. Previous experience with abstract algebra at the level of MATH 558 is recommended.

Arranged as needed to present appropriate material for groups of students. May be repeated for credit. Prerequisite: Variable.

Directed readings on a topic chosen by the student with the advice of an instructor. May be repeated for additional credit. Consent of the department required for enrollment.

Cauchy's theorem and contour integration; the argument principle; maximum modulus principle; Schwarz symmetry principle; analytic continuation; monodromy theorem; applications to the gamma function and Riemann's zeta function; entire and meromorphic functions; conformal mapping; Riemann mapping theorem; univalent functions. Prerequisite: MATH 766 or concurrently with MATH 766.

Axiomatic set theory; transfinite induction; regularity and choice; ordinal and cardinal arithmetic; miscellaneous additional topics (e.g., extra axioms such as GCH or MA; infinite combinatorics; large cardinals). Prerequisite: MATH 765 or MATH 791, or concurrent enrollment in MATH 765 or MATH 791, or equivalent evidence of mathematical maturity.

Measurable spaces and functions. Measure spaces and integration. Extensions of set functions, outer measures, Lebesgue measure. Signed and complex measures. Differentiation of set functions. Miscellaneous additional topics and applications. Prerequisite: MATH 766.

General topology. Set theory; topological spaces; connected sets; continuous functions; generalized convergence; product and quotient spaces; embedding in cubes; metric spaces and metrization; compact spaces; function spaces. Prerequisite: MATH 765.

The fundamental group and covering spaces (including classification); compact surfaces; homology theory, computations (including homotopy invariance) and applications (including Brouwer fixed point theorem); introduction to cohomology theory. Prerequisite: MATH 790 and MATH 791 and MATH 820, or permission of instructor.

An introduction to the fundamental structures and methods of modern algebraic combinatorics. Topics include partially ordered sets and lattices, matroids, simplicial complexes, polytopes, hyperplane arrangements, partitions and tableaux, and symmetric functions. Prerequisite: MATH 724 and MATH 791, or permission of the instructor.

This is an introductory course covering the basics of module theory over commutative rings. Topics include quotient modules and module homomorphisms; direct sums and free modules; tensor products of modules and exact sequences; projective, injective, and flat modules; direct and inverse limits of modules; the theory of modules over principal ideal domains, and normal forms; graded rings and modules. Prerequisite: MATH 790 and MATH 791.

This course covers foundational topics in commutative algebra not covered in MATH 830. Potential topics include integral extensions, lying over and going-up; normal rings and going-down; Noether normalization, and dimension theory for finitely generated algebras over a field; chain conditions, and Noetherian and Artinian rings and modules; local rings and Nakayama's Lemma; rings of formal power series; completion and flatness; primary decomposition and associated primes; affine algebraic varieties and Hilbert's Nullstellensatz; the prime spectrum of a ring and the Zariski topology. Prerequisite: MATH 830.

Multilinear algebra of finite dimensional vector spaces over fields; differentiable structures and tangent and tensor bundles; differentiable mappings and differentials; exterior differential forms; curves and surfaces as differentiable manifolds; affine connections and covariant differentiation; Riemannian manifolds. Prerequisite: MATH 765 and MATH 790.

Discrete and differentiable dynamical systems with an emphasis on the qualitative theory. Topics to be covered include review of linear systems, existence and uniqueness theorems, flows and discrete dynamical systems, linearization (Hartman-Grobman theorem), stable and unstable manifolds, Poincare sections, normal forms, Hamiltonian systems, and an introduction to bifurcation theory and chaos. Prerequisite: MATH 320 and MATH 766, or permission of instructor.

Topics to be covered include complex dynamical systems, perturbation theory, nonlinear analysis of time series, chaotic dynamical systems, and numerical methods as dynamical systems. Topics may vary. Course may be repeated if topic varies. Prerequisite: MATH 850 or permission of instructor.

Markov chains; Markov processes; diffusion processes; stationary processes. Emphasis is placed on applications: random walks; branching theory; Brownian motion; Poisson process; birth and death processes. Prerequisite: MATH 627 and MATH 765.

This is a second course in stochastic processes, focused on stochastic calculus with respect to a large class of semi-martingales and its applications to topics selected from classical analysis (linear PDE), finance, engineering, and statistics. The course will start with basic properties of martingales and random walks and then develop into the core program on Ito's stochastic calculus and stochastic differential equations. These techniques provide useful and important tools and models in many pure and applied areas. Prerequisite: MATH 727 and MATH 865.

Game theory, admissible decision functions and complete class theorems; Bayes and minimax solutions; sufficiency; invariance; multiple decision problems; sequential decision problems. Prerequisite: MATH 628 and MATH 766.

Advanced topics in numerical linear algebra including pseudo-spectra, rounding error analysis and perturbation theory, numerical methods for problems with special structure, and numerical methods for large scale problems. Topics may vary. Course may be repeated if topic varies. Prerequisite: MATH 781, MATH 782, MATH 790, or permission of the instructor.

Advanced course in the numerical solution of ordinary and partial differential equations including modern numerical methods and the associated analysis. Topics may vary. Course may be repeated if topic varies. Prerequisite: MATH 781, MATH 782, MATH 783, or permission of the instructor.

Introduction to modern techniques in Fourier Analysis in the Euclidean setting with emphasis in the study of functions spaces and operators acting on them. Topics may vary from year to year and include, among others, distribution theory, Sobolev spaces, estimates for fractional integrals and fractional derivatives, wavelets, and some elements of Calderón-Zygmund theory. Applications in other areas of mathematics, in particular partial differential equations and signal analysis, will be presented based on the instructor's and the students' interests. Prerequisite: MATH 810 and MATH 800, or instructor's permission.

Algebraic sets, varieties, plane curves, morphisms and rational maps, resolution of singularities, Reimann-Roch theorem. Prerequisite: MATH 790 and MATH 791.

General properties of Lie groups, closed subgroups, one-parameter subgroups, homogeneous spaces, Lie bracket, Lie algebras, exponential map, structure of semi-simple Lie algebras, invariant forms, Maurer-Cartan equation, covering groups, spinor groups. Prerequisite: MATH 766 and MATH 790 and MATH 791.

Probability measures, random variables, distribution functions, characteristic functions, types of convergence, central limit theorem. Laws of large numbers and other limit theorems. Conditional probability, Markov processes, and other topics in the theory of stochastic processes. Prerequisite: MATH 810.

Introduction; equations of mathematical physics; classification of linear equations and systems. Existence and uniqueness problems for elliptic, parabolic, and hyperbolic equations. Eigenvalue problems for elliptic operators; numerical methods. Prerequisite: MATH 766.

The course uses functional analytic techniques to further develop various aspects of the modern framework of linear and nonlinear partial differential equations. Sobolev spaces, distributions and operator theory are used in the treatment of linear second-order elliptic, parabolic, and hyperbolic equations. In particular we discuss the kind of potential, diffusion and wave equations that arise in inhomogeneous media, with an emphasis on the solvability of equations with different initial/boundary conditions. Then, we will survey the theory of semigroup of operators, which is one of the main tools in the study of the long-time behavior of solutions to nonlinear PDE. The theories and applications encountered in this course will create a strong foundation for studying nonlinear equations and nonlinear science in general. Topics may vary. Course may be repeated if topic varies. Prerequisite: MATH 950 or permission of the instructor.

Topological vector spaces, Banach spaces, basic principles of functional analysis. Weak and weak-topologies, operators and adjoints. Hilbert spaces, elements of spectral theory. Locally convex spaces. Duality and related topics. Applications. Prerequisite: MATH 810 and MATH 820 or concurrent with MATH 820.

Continuation of MATH 960. Topics may vary. Course may be repeated if topic varies.

Advanced courses on special topics; given as need arises. Prerequisite: Variable.