Teaching
Spring 2023 – MA 493 – Mathematical Foundations of Data Science
This course will cover foundational mathematical concepts fundamental to data science and data-driven mathematical modeling. The course will include the following topics: basics of machine learning, unconstrained optimization, neural networks and overfitting, parameter estimation and sensitivity analysis for mathematical models, and an introduction to topological data analysis.
Prerequisites: MA242 and MA305 (or MA 405). Some familiarity with MATLAB will be beneficial
Courses I have taught in the past several years include:
MA 242 Calculus III
MA 341 Applied Differential Equations I
MA 401 Applied Differential Equations II
MA 450 Methods of Applied Mathematics I
MA 451 Methods of Applied Mathematics II
MA 493 Mathematical Foundations of Data Science
MA 501 Advanced Mathematics for Engineers and Scientists I
MA/BMA 573 Mathematical Modeling of Physical and Biological Processes I
MA/BMA 574 Mathematical Modeling of Physical and Biological Processes II
MA 587 Numerical Solution of Partial Differential Equations–Finite Element Method