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
-Universal approximation theorems,
-Stochastic gradient descent,
The financial applications include
-deep portfolio optimization,
-deep simulation and
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
This class is taught as a lecture complemented with exercises.
Exercise Series (30%)
Coding Project (15%)
Final oral exam (55%)
The exercises will be discussed each week.