MATH 789R - Reading Seminar on Mathematics of Machine Learning
In this seminar, we discuss one recent work at the interface of applied mathematics and machine learning with the goal of exposing new research questions.
CS 584 - Numerical Methods for Deep Learning
This course provides students with the mathematical background needed to analyze and further develop numerical methods at the heart of deep learning.
MATH 347 - Introduction to Nonlinear Optimization
This advanced undergraduate course introduces nonlinear optimization problems, optimality conditions, and examples from different domains including finance, machine learning, and imaging.
MATH 571 - Numerical Optimization
This course provides students with an overview of state-of-the-art numerical methods for solving both unconstrained and constrained, large-scale optimization problems.
MATH 315 - Numerical Analysis
This undergraduate course provides an introduction to numerical methods (linear systems, data fitting, differentiation, integration, root finding, and minimization) and scientific computing using MATLAB.
Numerical Methods for Deep Learning
Mini-course most recently held at the Scuola Normale Superiore, Pisa (2019) and previously at the TU Berlin (2017) and TU Chemnitz (2018).
Numerical Methods for PDE-Constrained Optimization
This short course, held at the Doktorandenkolleg in Weissensee, gives an introduction into numerical methods for PDE-constrained optimization.
MATH 516 - Numerical Analysis II
This course, which is part two of our three-part graduate sequence on numerical analysis, focusses on optimization, root finding, interpolation, differentiation, integration, and differential equations.
MATH 789R - Bayesian Inverse Problems and Uncertainty Quantification
This special topics course introduces basic concepts as well as more recent advances in Bayesian methods for solving inverse problems.
MATH 211 - Multivariable Calculus
Third part of our standard calculus sequence.
MATH 346 - Introduction to Optimization Theory
This undergraduate course provides the fundamental theory for optimization problems (linear, quadratic, nonlinear, combinatorial).