Your application should demonstrate that you will benefit from the following curriculum. The minimal prerequisites for the master’s program are a solid background multi-variate calculus, linear/matrix algebra, and elementary probability and statistics.

The online application for the 2020-21 academic year is currently available.


Students can select one of the following tracks. Each track includes a sequence of three courses.
(i) Applied Analysis and Modeling Track:
• Applied Functional Analysis
• Partial Differential Equation
• Dynamical Systems

(ii) Computational Mathematics Track:
• Math Comp I: Linear Algebra and Matrix Computation
• Math Comp II: Optimization
• Machine Learning

Students will complete three additional courses of their choice. They may select from the track they did not pursue above or from the list below:
o Applied Stochastic Processes
o Inverse Problems and Imaging
o Multivariate Data Analysis via Matrix Decompositions
o Probability sequence; two or three courses, upon faculty approval,
o Numerical Methods for PDEs
o Modern Inference
o Computational Biology, upon faculty approval
o Uncertainty Quantification
o Fourier and Wavelet Analysis

For the remaining three courses, students can select from the above lists or from graduate-level courses related to CAM offered through the Physical Science Division, TTIC, or the Booth Business School.

Students pursuing the option of a master’s degree with thesis are required to:
(i) complete the above requirements; and
(ii) write and defend a master’s thesis under the guidance of a CAM advisor.

The option without master’s thesis may be completed in nine months (three full-time quarters). The option with a master’s thesis may be completed in 15 months (four full-time quarters, excluding summer). Students interested in pursuing a PhD program afterwards are encouraged to register for two years.



Courses are taught by faculty from:

The Committee on Computing and Applied Mathematics
The Department of Statistics
The Department of Mathematics
The Department of Computer Science