This course lays the theoretical and algorithmic foundations of convex optimization problems. We provide a fairly general understanding of a wide class of problems including linear programming, quadratic programming, and geometric programming.
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.
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.
This course provides students with the mathematical background needed to analyze and further develop numerical methods at the heart of deep learning.
This advanced undergraduate course introduces nonlinear optimization problems, optimality conditions, and examples from different domains including finance, machine learning, and imaging.
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.
This course provides students with an overview of state-of-the-art numerical methods for solving both unconstrained and constrained, large-scale optimization problems.
This undergraduate course provides an introduction to numerical methods (linear systems, data fitting, differentiation, integration, root finding, and minimization) and scientific computing using MATLAB.
This special topics course introduces basic concepts as well as more recent advances in Bayesian methods for solving inverse problems.
Third part of our standard calculus sequence.
This undergraduate course provides the fundamental theory for optimization problems (linear, quadratic, nonlinear, combinatorial).