The lecture introduces several fundamental concepts from machine learning as well as deep learning and treats important  applications  in finance. It will cover topics like

-Neural networks

-Universal approximation theorems,

-Stochastic gradient descent,

-Backpropagation.

The financial applications include

-deep hedging,

-deep portfolio optimization,

-deep simulation and

-deep calibration.

After completing this class the student will have the ability to...

-theoretically understand  neural networks, stochastic gradient descent, reservoir computing, etc.

-apply modern machine learning techniques to solve problems arising in quantitative finance, like hedging, portfolio optimization, prediction and calibration tasks

Standard rules for PIs

Exercise Series (30%)

Coding Project  (15%)