Course Descriptions

Lower Division Courses

AMS 2: Pre-Statistics
Reviews and introduces mathematical methods useful in the elementary study of statistics, including logic, real numbers, inequalities, linear and quadratic equations, functions, graphs, exponential and logarithmic functions, and summation notation. Prerequisite(s): Mathematics 2 or placement exam score of 20 or higher. (General Education Code(s):Q.) B. Mendes, The Staff

AMS 3: Precalculus for Science and Engineering
Includes real and complex numbers, inequalities, linear and quadratic equations, functions, graphs, exponential and logarithmic functions, trigonometry, and analytic geometry, with applications in science and engineering. Students cannot receive credit for both this course and Mathematics 2AB or 3. Mathematics 3 can substitute for course 3. Prerequisite(s): score of 20 or higher on Mathematics Placement Exam or Mathematics 2. (General Education Code(s): Q.) B. Mendes, The Staff

AMS 5: Statistics
Introduction to statistical methods/reasoning, including descriptive methods, data-gathering (experimental design and sample surveys), probability, interval estimation, significance tests, one- and two-sample problems, categorical data analysis, correlation and regression. Emphasis on applications to the natural and social sciences. Students cannot receive credit for this course if they have already received credit for course 7. (General Education Code(s): IN, Q.) A. Kottas, H. Lee, R. Morris, A. Rodriguez, B. Sanso, The Staff

AMS 7: Statistical Methods for the Biological, Environmental, and Health Sciences
Case-study-based introduction to statistical methods as practiced in the biological, environmental, and health sciences. Descriptive methods, experimental design, probability, interval estimation, hypothesis testing, one- and two-sample problems, power and sample size calculations, simple correlation and simple linear regression, one-way analysis of variance, categorical data analysis. (Formerly Statistical Methods for the Biological and Environmental Sciences.) Prerequisite(s): Score of 31 or higher on mathematics placement exam, course 2 or 3 or 11A or Mathematics 3 or 11A or 19A or by permission of instructor. Concurrent enrollment in course 7L is required. (General Education Code(s): IN, Q.) D. Draper, H. Lee, R. Prado

AMS 7L: Statistical Methods for the Biological, Environmental, and Health Sciences Laboratory
Computer-based laboratory course in which students gain hands-on experience in analysis of data sets arising from statistical problem-solving in the biological, environmental, and health sciences. Descriptive methods, interval estimation, hypothesis testing, one-and two-sample problems, correlation and regression, one-way analysis of variance, categorical data analysis. (Formerly Statistical Methods for the Biological and Environmental Sciences Laboratory.) Prerequisite(s): Score of 31 or higher on mathematics placement exam, course 2 or 3 or 11a or Mathematics 3 or 11A or 19A or by permission of instructor. Concurrent enrollment in course 7 is required. D. Draper, H. Lee, R. Prado

AMS 10: Mathematical Methods for Engineers I
Applications-oriented course on complex numbers and linear algebra integrating Matlab as a computational support tool. Introduction to complex algebra. Vectors, bases, and transformations; matrix algebra; solutions of linear systems, inverses and determinants; eigenvalues and eigenvectors; and geometric transformations. Students cannot receive credit for this course and for courses 10A or 27L or Mathematics 21. (Formerly course 27, Mathematical Methods for Engineers.) Prerequisite(s): Score of 40 or higher on mathematics placement exam, or course 3, or Mathematics 3. (General Education Code(s): Q.) N. Brummell, Y. Katznelson, B. Mendes, H. Wang, The Staff

AMS 10A: Basic Mathematical Methods for Engineers I
Applications-oriented course on complex numbers and linear algebra integrating Matlab as a computational support tool. Introduction to complex algebra. Vectors, bases, and transformations; matrix algebra; solutions of linear systems, inverses and determinants. Students cannot receive credit for this course and courses 10 or 27L or Mathematics 21. Prerequisite(s): Score of 40 or higher on mathematics placement exam, or course 3, or Mathematics 3. N. Brummell, Y. Katznelson, B. Mendes, H. Wang, The Staff

AMS 10B: Mathematical Methods for Engineers IB
Can only be taken by students who need a transition from course 10A to course 10. Students cannot get credit for this class and for course 10 or Mathematics 21 or courses 27 and 27L. Prerequisite(s): course 10A. Enrollment by permission of instructor only. N. Brummell, Y. Katznelson, B. Mendes, H. Wang, The Staff

AMS 11A: Mathematical Methods for Economists I
Introduction to mathematical tools and reasoning, with applications to economics. Topics are drawn from differential calculus in one variable and include limits, continuity, differentiation, elasticity, Taylor polynomials, and optimization. Students who have already taken Mathematics 11A and 19A should not take this course. (Also offered as Economics 11A. Students cannot receive credit for both courses.) Prerequisite(s): score of 31 or above on Mathematics Placement Exam. Students who do not place into precalculus should enroll in Math 2. (General Education Code(s): IN, Q.) Y. Katznelson, M. Mangel, D. Miluntinovic

AMS 11B: Mathematical Methods for Economists II
Mathematical tools and reasoning, with applications to economics. Topics are drawn from multivariable differential calculus and single variable integral calculus, and include partial derivatives, linear and quadratic approximation, optimization with and without constraints, Lagrange multipliers, definite and indefinite integrals, and elementary differential equations. (Also offered as Economics 11B. Students cannot receive credit for both courses.) Prerequisite(s): course 11A or Economics 11A. (General Education Code(s): IN, Q.) Y. Katznelson

AMS 15A: Case-Study Calculus I
Case-study-based, first-quarter introduction to single-variable calculus, with computing labs/discussion sections featuring contemporary symbolic, numerical, and graphical computing tools. Case studies drawn from biology, environmental sciences, health sciences, and psychology. Includes functions, mathematical modeling, limits, continuity, tangents, velocity, derivatives, the chain rule, implicit differentiation, higher derivatives, exponential and logarithmic functions and their derivatives, differentiating inverse functions, the mean value theorum, concavity, inflection points, function optimization, and curve-sketching. Students cannot receive credit for this course and course 11A or Economics 11A or Mathematics 11A or 19A. Prerequisite(s): course 3 or Mathematics 3 or score of 40 or higher on precalculus placement exam or by permission of instructor. Concurrent enrollment in course 15L required. Gen. Ed. Code(s): IN, Q. B. Mendes, The Staff

AMS 15B: Case-Study Calculus II
Case-study based, second-quarter introduction to single-variable calculus, with computing labs/discussion sections featuring symbolic numerical, and graphical computing tools. Case studies are drawn from biology, environmental science, health science, and psychology. Includes indefinite and definite integrals of functions of a single variable; the fundamental theorem of calculus; integration by parts and other techniques for evaluating integrals; infinite series; Taylor series, polynomial approximations. Students cannot receive credit for this course and course 11B or Economics 11B or Mathematics 11B of 19B. Prerequisite(s): course 15A or 11A or Economics 11A or Mathematics 11A or 19A. Gen Ed Code(s): IN, Q. B. Mendes, The Staff

AMS 20: Mathematical Methods for Engineers II
Applications-oriented class on ordinary differential equations (ODEs) and systems of ODEs using Matlab as a computational support tool. Covers linear ODEs and systems of linear ODEs; nonlinear ODEs using substitution and Laplace transforms; phase-plane analysis; introduction to numerical methods. Students cannot receive credit for this course and for courses 20A or 27L or Mathematics 24. Prerequisite(s): Mathematics 19B, and course 10 or 10A or Mathematics 21. Q. Gong, Y. Katznelson

AMS 20A: Basic Mathematical Methods for Engineers II
Applications-oriented class on ordinary differential equations (ODEs) and systems of ODEs integrating Matlab as a computational support tool. Covers linear ODEs and systems of linear ODEs; nonlinear ODEs using substitution and Laplace transforms. Students cannot receive credit for this course and for courses 20 or 27L or Mathematics 24. Prerequisites: Mathematics 19B, and course 10 or 10A or Mathematics 21. Q. Gong, Y. Katznelson

AMS 20B: Mathematical Methods for Engineers IIB
Can only be taken by students who need a transition from course 20A to course 20. Students cannot get credit for this course and for course 20 or Mathematics 24 or courses 27 and 27L. Prerequisite(s): course 20A. Enrollment by permission of the instructor only. Q. Gong, Y. Katznelson

AMS 27L: Mathematical Methods for Engineers Laboratory
Introduction to Matlab and elementary programming. Covers visualization of functions and data; linear algebra and numerical solutions of differential equations using the supplied Matlab routines. Previous knowledge of linear algebra and differential equation is expected. (Formerly Mathematical Methods for Engineers Laboratory) Prerequisite(s): Mathematics 21 and 24 or permission of instructor. Enrollment is limited to 40. The Staff

AMS 80A: Gambling and Gaming
Games of chance and strategy motivated early developments in probability, statistics, and decision theory. Course uses popular games to introduce students to these concepts, which underpin recent scientific developments in economics, genetics, ecology, and physics. (General Education Code(s): Q.) A. Rodriguez

Upper Division Courses

AMS 100: Mathematical Methods for Engineers III
Applications-oriented course on complex analysis and partial differential equations using Maple as symbolic math software support. In addition, introduces Fourier Analysis, special functions, and asymptotic methods. Students cannot receive credit for this course and for Physics 116B or 116C. Prerequisite(s): course 20 or by instructor permission. Enrollment limited to 25.

AMS 107: Introduction to Fluid Dynamics
Covers fundamental topics in fluid dynamics; Euler and Lagrange descriptions of continuum dynamics; conservation laws for inviscid and viscous flows; potential flows; exact solutions of the Navier-Stokes equation; boundary layer theory; gravity waves. Students cannot receive credit for this course and Applied Mathematics and Statistics 217. (Also offered as Physics 107. Students cannot receive credit for both courses.) Prerequisite(s): course 27, or Physics 116 A-B-C or equivalent. N. Brummell, G. Glatzmaier

AMS 114: Introduction to Dynamical Systems
Introduction to continuous and discrete dynamical systems. Topics include: fixed points, stability, limit cycles, bifurcations, transition to and characterization of chaos, fractals. Examples are drawn from sciences and engineering. Students cannot get credit for this class and for AMS 214. Prerequisite(s): course 10 and 20 or MATH 21 and MATH 24. Enrollment restricted to sophomores, juniors, and seniors. P. Garaud

AMS 115: Stochastic Modeling in Biology
Application of differential equations, probability, and stochastic processes to problems in cell, organismal, and population biology. Topics include life-history theory, behavioral ecology, and population biology. Students may not receive credit for this course and course 215. Prerequisite(s): course 131, a university-level course in biology, and operational knowledge of a programming language; or consent of instructor. M. Mangel

AMS 131: Introduction to Probability Theory
Introduction to probability theory and its applications. Combinatorial analysis, axioms of probability and independence, random variables (discrete and continuous), joint probability distributions, properties of expectation, Central Limit Theorem, Law of Large Numbers, Markov chains. Students cannot receive credit for this course and Computer Engineering 107. Prerequisite(s): course 11B or Economics 11B or Mathematics 11B or 19B. (General Education Code(s): Q.) A. Kottas, M. Mangel, R. Prado, B. Sanso

AMS 132: Statistical Inference
Introduction to statistical inference at a calculus-based level: maximum likelihood estimation, sufficient statistics, distributions of estimators, confidence intervals, hypothesis testing, and Bayesian inference. Prerequisite(s): course 131 or Computer Engineering 107. A. Kottas, A. Rodriguez

AMS 147: Computational Methods and Applications
Applications of computational methods to solving mathematical problems using Matlab. Topics include solution of nonlinear equations, linear systems, differential equations, sparse matrix solver, and eigenvalue problems. Some prior experience with Matlab is helpful but not required. Knowledge of differential equations is recommended (course 27 or Mathematics 24). Prerequisite(s): course 27 or Mathematics 21. H. Wang

AMS 156: Linear Regression
Covers simple linear regression, multiple regression, and analysis of variance models. Students learn to use the software package R to perform the analysis, and to construct a clear technical report on their analysis, readable by either scientists or non-technical audiences. (Formerly Linear Statistical Models) Prerequisite(s): course 132. Gen. Ed. Code(s): W. Enrollment limited to 30. H. Lee

AMS 198: Independent Study or Research
Students submit petition to sponsoring agency. May be repeated for credit. The Staff

AMS 198F: Independent Study or Research
Students submit petition to sponsoring agency. May be repeated for credit. The Staff

Graduate Courses

AMS 200: Research and Teaching in AMS
Basic teaching techniques for teaching assistants, including responsibilities and rights, resource materials, computer skills, leading discussion or lab sessions, presentation techniques maintaining class records, and grading. Examines research and professional training, including use of library, technical writing, giving talks in seminars and conferences, and ethical issues in science and engineering. Enrollment restricted to graduate students. N. Brummell

AMS 202: Linear Models in SAS
Case study-based course teaches statistical linear modeling using the SAS software package. Teaches generalized linear models; linear regression; analysis of variance/covariance; analysis of data with random effects and repeated measures. Prerequisite(s): course 156 or 256, or permission of instructor. Enrollment restricted to graduate students. B. Mendes

AMS 205: Mathematical Statistics
Graduate introduction to topics in probability and mathematical statistics from the frequentist point of view: sufficiency, exponential families, maximum likelihood estimation, optimality theory for estimation, confidence intervals and significance testing, decision theory, convergence in probability and in law, central limit theorems, and efficiency and asymptotic normality. Enrollment restricted to graduate students. B. Sanso

AMS 205A: Mathematical Statistics
Graduate introduction to topics in probability and mathematical statistics. Probabilities, random variables, common families of distributions; expectation and higher moments; multivariate distributions, marginals and conditionals; point estimation, methods and properties; interval estimation, methods and properties; and hypothesis testing, methods and properties. (Formerly course 205.) Prerequisite(s): Strongly recommended: course 131. Previous experience with univariate and multivariate calculus, and experience with elementary probability also recommended. Enrollment restricted to graduate students. A. Rodriguez, B. Sanso

AMS 205B: Statistical Inference
Statistical inference from a frequentist point of view. Properties of random samples; convergence concepts applied to point estimators; principles of statistical inference; obtaining and evaluating point estimators with particular attention to maximum likelihood estimates and their properties; obtaining and evaluating interval estimators; and hypothesis testing methods and their properties. Prerequisite(s): course 131 or equivalent. Enrollment restricted to graduate students. B. Sanso

AMS 206: Bayesian Statistics
Introduction to Bayesian statistical methods for inference and prediction; exchangeability; prior, likelihood, posterior, and predictive distributions; coherence and calibration; conjugate analysis; Markov Chain Monte Carlo methods for simulation-based computation; hierarchical modeling; Bayesian model diagnostics, model selection, and sensitivity analysis. Prerequisite(s): graduate standing, or course 132, or permission of instructor. Enrollment restricted to juniors, seniors, and graduate students. H. Lee

AMS 207: Intermediate Bayesian Statistical Modeling
Hierarchical modeling, linear models (regression and analysis of variance) from the Bayesian point of view, intermediate Markov chain Monte Carlo methods, generalized linear models, multivariate models, mixture models, hidden Markov models. Prerequisite(s): course 206, and graduate standing or permission of instructor. D. Draper, R. Prado, B. Sanso

AMS 210: Mathematical Models
Serves a dual purpose: provides an introduction to the ideas underlying the mathematical modeling of physical phenomena; and in discussing the various phenomena, this course either reviews or introduces mathematical concepts and techniques. Models described chosen from diverse topics such as population dynamics, chemical reactions, fluid and solid mechanics, quantum theory, and probability. Mathematical techniques covered include advanced theory of ordinary and partial differential equations, eigenvalue problems, and linear stability theory. Enrollment restricted to graduate students or permission of instructor. The Staff

AMS 211: Foundations of Applied Mathematics
Accelerated class on applied mathematical methods for all sciences. Topics include: multivariate calculus, linear algebra, Fourier series, ordinary differential equations, complex analysis, and integral transforms. Enrollment restricted to graduate students. N. Brummell

AMS 212A: Applied Mathematical Methods I
Focuses on the analytical and numerical methods for solving differential equations. Topics include well-posed problems, Fourier transform, separation of variables, Green's functions, Huygen's principle, calculus of variation, numerical discretization, local truncation error, global error, error estimation, numerical stability, multigrid method. (Formerly course 211.) Enrollment restricted to graduate students. Undergraduates are encouraged to take this class with permission of instructor. N. Brummell, P. Garaud, H. Wang

AMS 212B: Applied Mathematical Methods II
Covers pertubation methods: asymptotic series, stationary phase and expansion of integrals, matched asymptotic expansions, multiple scales and the WKB method, Padé approximants and improvements of series. (Formerly course 212.) Prerequisite(s): course 212A. H. Wang, P. Garaud, N. Brummell

AMS 213: Numerical Solutions of Differential Equations
Focuses on numerical solutions of differential equations. Topics include Runge-Kutta methods; error estimation and error control; consistency, stability, and convergence; conjugate gradient method; multigrid method; CFL condition; and high-resolution methods for conservation laws. Enrollment restricted to graduate students or permission of instructor. H. Wang, P. Garaud, N. Brummell

AMS 214: Applied Dynamical Systems
Introduces continuous and discrete dynamical systems. Topics include: fixed points; stability; limit cycles; bifurcations; transition to and characterization of chaos; and fractals. Examples drawn from sciences and engineering; founding papers of the subject are studied. Students cannot receive credit for this course and course 114. Enrollment restricted to graduate students. Undergraduates are encouraged to enroll with permission of the instructor. P. Garaud, M. Mangel, H. Wang

AMS 215: Stochastic Modeling in Biology
Application of differential equations and probability and stochastic processes to problems in cell, organismal, and population biology. Topics include: life-history theory, behaviorial ecology, and population biology. Enrollment restricted to graduate students or permission of instructor. Students may not receive credit for this course and course 115. M. Mangel

AMS 216: Stochastic Differential Equations
Introduction to stochastic differential equations and diffusion processes with applications to biology, biomolecular engineering, and chemical kinetics. Topics include Brownian motion and white noise, gambler's ruin, backward and forward equations, and the theory of boundary conditions. Enrollment restricted to graduate students or consent of instructor. M. Mangel

AMS 217: Introduction to Fluid Dynamics
Covers fundamental topics in fluid dynamics; Euler and Lagrange descriptions of continuum dynamics; conservation laws for inviscid and viscous flows; potential flows; exact solutions of the Navier-Stokes equation. Boundary layer theory; gravity waves. Students cannot receive credit for this course and course 107. Enrollment restricted to graduate students. N. Brummell, G. Glatzmaier

AMS 221: Bayesian Decision Theory
Explores conceptual and theoretical bases of statistical decision making under uncertainty. Focuses on axiomatic foundations of expected utility, elicitation of subjective probabilities and utilities, and the value of information and modern computational methods for decision problems. Prerequisite(s): course 206. Enrollment restricted to graduate students. B. Sanso

AMS 223: Time Series Analysis
Graduate level introductory course on time series data and models in the time and frequency domains: descriptive time series methods; the periodogram; basic theory of stationary processes; linear filters; spectral analysis; time series analysis for repeated measurements; ARIMA models; introduction to Bayesian spectral analysis; Bayesian learning, forecasting, and smoothing; introduction to Bayesian Dynamic Linear Models (DLMs); DLM mathematical structure; DLMs for trends and seasonal patterns; and autoregression and time series regression models. Prerequisite: course 206. Enrollment restricted to graduate students. R. Prado

AMS 225: Multivariate Statistical Methods
Introduction to statistical methods for analyzing data sets in which two or more variables play the role of outcome or response. Descriptive methods for multivariate data. Matrix algebra and random vectors. The multivariate normal distribution. Likelihood and Bayesian inferences about multivariate mean vectors. Analysis of covariance structure: principle components, factor analysis. Discriminant, classification and cluster analysis. Prerequisite(s): course 206. Enrollment restricted to graduate students. D. Draper

AMS 231: Nonlinear Control Theory
Covers analysis and design of nonlinear control systems using Lyapunov theory and geometric methods. Includes properties of solutions of nonlinear systems, Lyapunov stability analysis, effects of perturbations, controllability, observability, feedback linearization, and nonlinear control design tools for stabilization. Prerequisite(s): basic knowledge of mathematical analysis and ordinary differential equations is assumed. Enrollment restricted to graduate students or permission of instructor. Q. Gong

AMS 236: Motion Coordination of Robotic Networks
Comprehensive introduction to motion coordination algorithms for robotic networks. Emphasis on mathematical tools to model, analyze, and design cooperative strategies for control, robotics, and sensing tasks. Topics include: continuous and discrete-time evolution models, proximity graphs, performance measures, invariance principles, and coordination algorithms for rendezvous, deployment, flocking, and consensus. Techniques and methodologies are introduced through application setups from multi-agent robotic systems, cooperative control, and mobile sensor networks. Enrollment restricted to graduate students. Enrollment limited to 15. The Staff

AMS 241: Bayesian Nonparametric Methods
Theory, methods, and applications of Bayesian nonparametric modeling. Prior probability models for spaces of functions. Dirichlet processes. Pólya trees. Nonparametric mixtures. Models for regression, survival analysis, categorical data analysis, and spatial statistics. Examples drawn from social, engineering, and life sciences. Prerequisite(s): course 207. Enrollment restricted to graduate students. A. Kottas, The Staff

AMS 245: Spatial Statistics
Introduction to the analysis of spatial data: theory of correlation structures and variograms; kriging and Gaussian processes; Markov random fields; fitting models to data; computational techniques; frequentist and Bayesian approaches. Prerequisite(s): course 207. Enrollment restricted to graduate students. B. Sanso, H. Lee

AMS 256: Linear Statistical Models
Theory, methods, and applications of linear statistical models. Review of simple correlation and simple linear regression. Multiple and partial correlation and multiple linear regression. Analysis of variance and covariance. Linear model diagnostics and model selection. Case studies drawn from natural, social, and medical sciences. Course 205 strongly recommended as a prerequisite. Undergraduates are encouraged to take this class with permission of instructor. Prerequisite(s): course 205A or 205B or permission of instructor. Enrollment restricted to graduate students. R. Prado, B. Sanso

AMS 261: Probability Theory with Markov Chains
Introduction to probability theory: probability spaces, expectation as Lebesgue integral, characteristic functions, modes of convergence, conditional probability and expectation, discrete-state Markov chains, stationary distributions, limit theorems, ergodic theorem, continuous-state Markov chains, applications to Markov chain Monte Carlo methods. Prerequisite(s): course 205B or by permission of instructor. Enrollment restricted to graduate students. A. Kottas

AMS 263: Stochastic Processes
Includes probabilistic and statistical analysis of random processes, continuous-time Markov chains, hidden Markov models, point processes, Markov random fields, spatial and spatio-temporal processes, and statistical modeling and inference in stochastic processes. Applications to a variety of fields. Prerequisite(s): course 205A, 205B, or 261, or permission of instructor. A. Kottas

AMS 274: Generalized Linear Models
Theory, methods, and applications of generalized linear statistical models; review of linear models; binomial models for binary responses (including logistical regression and probit models); log-linear models for categorical data analysis; and Poisson models for count data. Case studies drawn from social, engineering, and life sciences. Prerequisite(s): course 205A, 205B, or 256. Enrollment restricted to graduate students. A. Kottas

AMS 280A: Seminar in Mathematical and Computational Biology
Weekly seminar on mathematical and computational biology. Participants present research findings in organized and critical fashion, framed in context of current literature. Students present own research on a regular basis. Enrollment restricted to graduate students. Enrollment limited to 20. May be repeated for credit. M. Mangel

AMS 280B: Seminars in Statistical and Applied Mathematical Modeling
Weekly seminar series covering topics of current research in applied mathematics and statistics. Permission of instructor required. Enrollment restricted to graduate students. (Formerly "Seminar in Applied Mathematics and Statistics") May be repeated for credit. The Staff

AMS 280C: Seminar in Geophysical & Astrophysical Fluid Dynamics
Weekly seminars/discussion group on geophysical and astrophysical fluid dynamics, covering both analytical and computational approaches. Participants present research progress and findings in semi-formal discussions. Students must present their own research on a regular basis. Enrollment restricted to graduate students. May be repeated for credit. N. Brummell, P. Garaud

AMS 285: Seminar in Career Skills
Seminar in career skills for applied mathematicians and statisticians. Learn about professional activities such as the publication process, grant proposals, and the job market. Enrollment restricted to graduate students, typically within two years of their expected Ph.D. completion date. The Staff

AMS 290A: Topics in Mathematical and Computational Biology
Focuses on applications of mathematical and computational methods with particular emphasis on advanced methods applying to organismal biology or resource management. Students read current literature, prepare critiques, and conduct projects. Enrollment restricted to graduate students. Enrollment limited to 20. May be repeated for credit. M. Mangel

AMS 290B: Advanced Topics in the Numerical Solution of PDEs
Modern practical methods for the numerical solution of partial differential equations. Methods considered depend on the expertise of the instructor, but are covered in-depth and up to the cutting-edge of practical contemporary implementation. Content could be method-based (e.g., spectral methods, finite-element methods) or topic-based (e.g., simulations of turbulence). Some programming and numerical analysis (e.g., course 213) highly recommended. Enrollment restricted to graduate students and undergraduates with permission of the instructor. Enrollment restricted to graduate students and undergraduates with permission of the instructor. N. Brummell, P. Garaud, H. Wang

AMS 291: Advanced Topics in Bayesian Statistics
Advanced study of research topics in the theory, methods, or applications of Bayesian statistics. The specific subject depends on the instructor. Enrollment restricted to graduate students and by permission of instructor. May be repeated for credit. E. Anderson

AMS 296: Masters Project
Independent completion of a masters project under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students. May be repeated for credit. The Staff

AMS 297: Independent Study or Research
Independent study or research under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students. The Staff

AMS 297F: Independent Study
Independent study or research under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students. May be repeated for credit. The Staff

AMS 299: Thesis Research
Thesis research under faculty supervision. Students submit petition to sponsoring agency. Enrollment restricted to graduate students. The Staff