Curriculum: Required Quantitative Elective (3 credits/9 units)
| CMU 10-702 | Statistical Machine Learning |
| CMU 10-708 | Probabilistic Graphical Models |
| CMU 15-750 | Algorithms |
| CMU 15-853 | Algorithms in the Real World |
| CMU 15-859G | Computational Geometry |
| CMU 18-798 | Image, Video, and Multimedia |
| CMU 21-660 | Introduction to Numerical Analysis |
| CMU 21-661 | Numerical Solution of Partial Differential Equations I |
| CMU 21-662 | Numerical Solution of Partial Differential Equations II |
| CMU 21-732 | Partial Differential Equations I |
| CMU 21-737 | Probabilistic Combinatorics |
| CMU 36-625 | Probability and Mathematical Statistics |
| CMU 36-705 | Intermediate Statistics |
| CMU 36-753/754 | Probability Theory and Stochastic Processes |
| Pitt BIOINF 2101 | Probabilistic Models for Computer-Based Decision Support |
| Pitt BIOST 2015 | Elements of Statistical Learning |
| Pitt BIOST 2043 | Intro to Statistical Theory |
| Pitt BIOST 2063 | Bayesian and Empirical Statistical Methods |
| Pitt BIOST 2064 | Bayesian and Empirical Bayes Computational Methods |
| Pitt CS 2550 | Principles of Database Design |
| Pitt MATH 2030 | Iterative Methods for Linear Systems |
| Pitt MATH 2070 | Numerical Methods in Scientific Computing |
| Pitt MATH 2090 | Numerical Solution of Ordinary Differential Equations |
| Pitt MATH 2370 | Matrices and Linear Operators (Linear Algebra) |
| Pitt STAT 2631 | Theory of Statistics |
| Pitt STAT 2711 | Probability Theory I |