Optimization for Data Science

About

The course provides an overview of modern optimization methods with a focus on applications in economics, machine learning, and data science. Special attention will be given to the scalability of algorithms to large datasets, both in theory and implementation.

Preliminary course structure:
1. Convex optimization

Optimization problems
Convex sets, convex functions, convex optimization problems
Lagrangian duality
Optimality conditions
Applications in econometrics, statistics, and machine learning

2. Algorithms

Gradient descent
Projected gradient descent
Stochastic gradient descent
Newton methods

Course leader

Tobias Sutter

Target group

Master | PhD | Postdocs | Professionals

Fee info