课程安排
Core & Foundational Requirements
AMS 212A Applied Mathematical Methods I
AMS 213A Numerical Linear Algebra
AMS 213B Numerical Methods for the Solution of Differential equations
AMS 214 Applied Dynamical Systems
AMS 250 An Introduction to High Performance Computing
备注：
Students in the SciCAM program must also demonstrate mastery in the foundations of Scientific Computing and Applied Mathematics, either by producing evidence through undergraduate transcripts, or by taking some or all of the following foundational courses upon entry to the M.S. program: AMS 147 (Computational Methods and Applications), AMS 209 (Foundations of Scientific Computing) and AMS 211 (Foundations of Applied Mathematics).
Electives And Capstone Requirements
联系自己的graduate director
全部课程查询
AMS2: Pre-Statistics
AMS3: Precalculus for the Social Sciences
AMS5: Statistics
AMS6: Precalculus for Statistics
AMS7: Statistical Methods for the Biological, Environmental, and Health Sciences
AMS7L: Statistical Methods for the Biological, Environmental, and Health Sciences Laboratory
AMS10: Mathematical Methods for Engineers I
AMS10A: Basic Mathematical Methods for Engineers I
AMS10B: Mathematical Methods for Engineers IB
AMS11A: Mathematical Methods for Economists I
AMS11B: Mathematical Methods for Economists II
AMS15A: Case-Study Calculus I
AMS15B: Case-Study Calculus II
AMS20: Mathematical Methods for Engineers II
AMS20A: Basic Mathematical Methods for Engineers II
AMS20B: Mathematical Methods for Engineers IIB
AMS27: Mathematical Methods for Engineers
AMS27L: Mathematical Methods for Engineers Laboratory
AMS80A: Gambling and Gaming
AMS80B: The Art of Data Visualization
AMS100: Mathematical Methods for Engineers III
AMS107: Introduction to Fluid Dynamics
AMS113: Managerial Statistics
AMS114: Introduction to Dynamical Systems
AMS115: Stochastic Modeling in Biology
AMS118: Estimation and Introduction to Control of Stochastic Processes
AMS129: Foundations of Scientific Computing for Scientists and Engineers
AMS131: Introduction to Probability Theory
AMS132: Classical and Bayesian Inference
AMS146: Introduction to Dynamical Systems
AMS147: Computational Methods and Applications
AMS148: GPU Programming for Scientific Computations
AMS156: Linear Regression
AMS162: Design and Analysis of Computer Simulation Experiments
AMS198: Independent Study or Research
AMS198F: Independent Study or Research
AMS200: Research and Teaching in AMS
AMS202: Linear Models in SAS
AMS203: Introduction of Probability Theory
AMS204: Introduction to Statistical Data Analysis
AMS205: Mathematical Statistics
AMS205A: Mathematical Statistics
AMS205B: Intermediate Classical Inference
AMS206: Classical and Bayesian Inference
AMS206B: Intermediate Bayesian Inference
AMS207: Intermediate Bayesian Statistical Modeling
AMS209: Foundations of Scientific Computing
AMS210: Mathematical Models
AMS211: Foundations of Applied Mathematics
AMS212A: Applied Mathematical Methods I
AMS212B: Applied Mathematical Methods II
AMS213: Numerical Solutions of Differential Equations
AMS213A: Numerical Linear Algebra
AMS213B: Numerical Methods for the Solution of Differential equations
AMS214: Applied Dynamical Systems
AMS215: Stochastic Modeling in Biology
AMS216: Stochastic Differential Equations
AMS217: Introduction to Fluid Dynamics
AMS218: Estimation and Introduction to Control of Stochastic Processes
AMS221: Bayesian Decision Theory
AMS223: Time Series Analysis
AMS225: Multivariate Statistical Methods
AMS227: Waves and Instabilities in Fluids
AMS229: Convex Optimization
AMS230: Numerical Optimization
AMS231: Nonlinear Control Theory
AMS232: Applied Optimal Control
AMS236: Motion Coordination of Robotic Networks
AMS238: Fundamentals of Uncertainty Quantification in Computational Science and Engineering
AMS241: Bayesian Nonparametric Methods
AMS245: Spatial Statistics
AMS250: An Introduction to High Performance Computing
AMS256: Linear Statistical Models
AMS260: Computational Fluid Dynamics
AMS261: Probability Theory with Markov Chains
AMS263: Stochastic Processes
AMS266A: Data Visualization and Statistical Programming in R
AMS266B: Advanced Statistical Programming in R
AMS266C: Introduction to Data Wrangling
AMS268: Advanced Bayesian Computation
AMS274: Generalized Linear Models
AMS275: Magnetohydrodynamics
AMS276: Bayesian Survival Analysis and Clinical Trial Design
AMS280A: Seminar in Mathematical and Computational Biology
AMS280B: Seminars in Statistical and Applied Mathematical Modeling
AMS280C: Seminar in Geophysical & Astrophysical Fluid Dynamics
AMS280D: Seminar in Bayesian Statistical Methodology
AMS285: Seminar in Career Skills
AMS290A: Topics in Mathematical and Computational Biology
AMS290B: Advanced Topics in the Numerical Solution of PDEs
AMS291: Advanced Topics in Bayesian Statistics
AMS296: Masters Project
AMS297: Independent Study or Research
AMS297F: Independent Study
AMS299: Thesis Research
