For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).
Registration via LPIS
|Thursday||03/02/23||01:00 PM - 04:30 PM||TC.3.12|
|Thursday||03/16/23||01:00 PM - 04:30 PM||TC.3.12|
|Thursday||03/23/23||01:00 PM - 04:30 PM||TC.3.12|
|Thursday||03/30/23||01:00 PM - 04:30 PM||TC.3.12|
|Thursday||04/13/23||01:00 PM - 04:30 PM||TC.3.12|
|Thursday||04/20/23||01:00 PM - 04:30 PM||TC.3.12|
|Thursday||04/27/23||01:00 PM - 04:30 PM||TC.3.09|
|Thursday||05/04/23||01:00 PM - 03:00 PM||TC.3.03|
The lecture will be held on campus unless activity on campus becomes unavailable. Should activity on campus becomes impossible then students will be informed about the new mode of teaching.
The lecture introduces several fundamental concepts from machine 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 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.
- 80,1% or more (33-40 points): 1
- 70,1% - 80% (29-32 points): 2
- 60,1% - 70% (25-28 points): 3
- 50,1% - 60% (21-24 points): 4
- 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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