Applied Mathematics and Statistics Master’s Student Handbook

Probability Theory:

110.445 Mathematical and Computational Foundations of Data Science; 553.626 Introduction to Stochastic Processes; 553.627 Introduction to Stochastic Processes in Finance I; 553.628 Introduction to Stochastic Processes in Finance II; 553.629 Introduction to Research in Discrete Probability (until Summer 2018 only); 553.633 Monte Carlo Methods; 553.720 Probability Theory I; 553.721 Probability Theory II; 553.722 Introduction to Stochastic Calculus; 553.763 Stochastic Search and Optimization; 553.764 Models, Simulation and Monte Carlo.

Statistics and Statistical Learning:

110.445 Mathematical and Computational Foundations of Data Science; 553.602 Research and Design in Applied Mathematics: Data Mining; 553.613 Applied Statistics and Data Analysis; 553.614 Applied Statistics and Data Analysis II; 553.616 Intro to Statistical Learning, Data Analysis and Signal Processing; 553.617 Mathematical Modeling: Statistical Learning; 553.632 Bayesian Statistics; 553.636 Introduction to Data Science; 553.639 Time Series Analysis; 553.650 Computational Molecular Medicine; 553.669 Large-Scale Optimization for Data Science; 553.730 Statistical Theory I; 553.731 Statistical Theory II; 553.733 Advanced Bayesian Statistics; 553.735 Topics in Statistical Pattern Recognition; 553.737 Distribution-free Statistics and Resampling Methods; 553.738 High Dimensional Approximation, Probability, and Statistical Learning; 553.739 Statistical Pattern Recognition Theory & Methods; 553.740 Machine Learning I; 553.741 Machine Learning II; 553.743 Equivariant Machine Learning; 553.742 Statistical Inference on Graphs; 553.767 Iterative Algorithms in Data Science; 553.782 Statistical Uncertainty Quantification.

Optimization and Operations Research:

553.600 Mathematical Modeling and Consulting; 553.653 Mathematical Game Theory; 553.661 Optimization in Finance; 553.662 Optimization in Data Science; 553.663 Network Models in Operations Research; 553.665 Introduction to Convexity; 553.667 Deep Learning in Discrete Optimization; 553.669 Large-Scale Optimization for Data Science; 553.761 Nonlinear Optimization I; 553.762 Nonlinear Optimization II; 553.763 Stochastic Search and Optimization; 553.765 Convex Optimization; 553.766 Combinatorial Optimization; 553.767 Iterative Algorithms in Data Science; 553.769 Topics in Discrete Optimization; 553.797 Introduction to Control Theory and Optimal Control.

Computational and Applied Mathematics:

110.445 Mathematical and Computational Foundations of Data Science; 553.681 Numerical Analysis; 553.688 Computing for Applied Mathematics; 553.691  Dynamical Systems; 553.692 Mathematical Biology; 553.693 Mathematical Image Analysis; 553.694 Applied and Computational Multilinear Algebra; 553.780 Shape and Differential Geometry; 553.784 Mathematical Foundations of Computational Anatomy; 553.792 Matrix Analysis; 553.793 Turbulence Theory; 553.795 Advanced Parameterization (through Fall 2022); 553.795 Matrix Analysis and Linear Algebra II (as of Spring 2023):

Discrete Mathematics:

At least one of:

553.671 Combinatorial Analysis; 553.672 Graph Theory; 553.766 Combinatorial Optimization;

but the other two courses may include 553.629 Introduction to Research in Discrete Probability (until Summer 2018 only) and the Computer Science offerings:

601.630 Combinatorics & Graph Theory in Computer Science; 601.631 Theory of Computation; 601.633 Algorithms I; 601.634 Randomized Algorithms; 601.635 Approximation Algorithms; 601.645 Practical Cryptographic Systems.

This list of courses is based on recent offerings.  Not all classes are available every year, and substitute classes may be accepted if approved by the advisor and the Academic Affairs Committee.