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Nr. LV-Typ(en) LV-Titel
5515 PI Machine Learning in Finance Präsenz-Modus
Anmeldung über LPIS
vom 01.02.2024 16:00 bis 18.02.2024 23:59
Abmeldung über LPIS
vom 01.02.2024 16:00 bis 04.03.2024 23:59

LV-Leiter/in Assist.Prof. Priv.Doz.Dr. Paul Eisenberg
Planpunkte Master Wahlfach - Advanced Topics in Statistics and Computing
Semesterstunden 2
Unterrichtssprache Englisch

Termine
Do, 07.03.2024 13:00-16:30 Uhr D4.0.127 (Lageplan)
Do, 14.03.2024 13:00-16:30 Uhr D4.0.127 (Lageplan)
Do, 21.03.2024 13:00-16:30 Uhr D4.0.127 (Lageplan)
Do, 11.04.2024 13:00-16:30 Uhr D4.0.127 (Lageplan)
Do, 18.04.2024 13:00-16:30 Uhr D4.0.127 (Lageplan)
Do, 25.04.2024 13:00-16:30 Uhr D4.0.127 (Lageplan)
Do, 02.05.2024 13:00-15:00 Uhr P D4.0.022 (Lageplan)
Termindownload (ical) | Termine abonnieren

Weitere Informationen https://learn.wu.ac.at/vvz/24s/5515

Kontakt:
paul.eisenberg@wu.ac.at
Inhalte der LV:

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.
Lernergebnisse (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.
Regelung zur Anwesenheit:

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.
Lehr-/Lerndesign:

This class is taught as a lecture complemented with exercises and a coding project.
Leistung(en) für eine Beurteilung:

Exercise Series (30%)

Coding project (25%)

Final exam (45%)

Grade:

  1. 85,1% or more: 1
  2. 75,1% - 85%: 2
  3. 65,1% - 75%: 3
  4. 55,1% - 65%: 4
  5. 0 - 55%: failed
Teilnahmevoraussetzung(en):
  • Successful completion of at least 42 ECTS credits from the first year compulsory common courses
  • Allocation to the elective
Zuletzt bearbeitet: 22.03.2024 11:12
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