The lecture introduces several fundamental concepts from machine 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.

This class is taught as a lecture complemented with exercises and a coding project.

Exercise Series (25%)

Coding project (20%)

Final exam (55%)

There will be a total of 40 points (10 Exercises, 8 Coding project, 22 Exam) and the final grade is based on this points.

Grade:

1. 80,1% or more (33-40 points): 1
2. 70,1% - 80% (29-32 points): 2
3. 60,1% - 70% (25-28 points): 3
4. 50,1% - 60% (21-24 points): 4
5. 0 - 50% ( 0-20 points): failed

• Successful completion of at least 42 ECTS credits from the first year compulsory common courses
• Allocation to the elective

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