5699 Y2E Machine Learning in Finance
Assist.Prof. Priv.Doz.Dr. Paul Eisenberg
Contact details
Weekly hours
Language of instruction
02/01/23 to 02/17/23
Registration via LPIS
Notes to the course
Day Date Time Room
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.

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,


The financial applications include

-deep hedging,

-deep portfolio optimization,

-deep simulation and

-deep calibration.

Learning outcomes

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.

Attendance requirements

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.
Teaching/learning method(s)

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.


  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
Prerequisites for participation and waiting lists
  • Successful completion of at least 42 ECTS credits from the first year compulsory common courses
  • Allocation to the elective

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at

Recommended previous knowledge and skills
Successful completion of the first-year courses in the Master QFin.

Last edited: 2022-11-03