For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).
Students are expected to be active in the class.
Syllabus
Title
5371 Machine Learning in Finance
Instructors
Assist.Prof. Priv.Doz.Dr. Paul Eisenberg
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/03/25 to 02/16/25
Registration via LPIS
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
| Day | Date | Time | Room |
|---|---|---|---|
| Thursday | 03/06/25 | 01:00 PM - 04:30 PM | D4.0.127 |
| Thursday | 03/13/25 | 01:00 PM - 04:30 PM | D4.0.133 |
| Thursday | 03/20/25 | 01:00 PM - 04:30 PM | D4.0.133 |
| Thursday | 03/27/25 | 01:00 PM - 04:30 PM | D4.0.133 |
| Thursday | 04/03/25 | 01:00 PM - 04:30 PM | D4.0.133 |
| Thursday | 04/10/25 | 01:00 PM - 04:30 PM | D4.0.133 |
| Thursday | 04/24/25 | 01:00 PM - 04:30 PM | D4.0.127 |
| Thursday | 05/08/25 | 12:00 PM - 02:00 PM | TC.3.01 |
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.
Students are expected to be active in the class.
Attendance during classes is mandatory.
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 (30%)
Coding project (25%)
Final exam (45%)
Grade:
- 85,1% or more: 1
- 75,1% - 85%: 2
- 65,1% - 75%: 3
- 55,1% - 65%: 4
- 0 - 55%: failed
- Successful completion of at least 42 ECTS credits from the first year compulsory common courses
- Allocation to the elective
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Last edited: 2024-11-11
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