Syllabus

Titel
2297 Machine Learning in Finance
LV-Leiter/innen
Christa Cuchiero, Ph.D.
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
16.09.2019 bis 07.10.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Doktorat/PhD
Termine
Wochentag Datum Uhrzeit Raum
Montag 07.10.2019 13:00 - 15:30 TC.3.06
Montag 14.10.2019 13:00 - 15:30 TC.3.06
Montag 21.10.2019 13:00 - 15:30 TC.3.06
Montag 28.10.2019 13:00 - 15:30 TC.3.06
Montag 04.11.2019 13:00 - 15:00 TC.3.08
Montag 11.11.2019 13:00 - 15:00 TC.3.06
Donnerstag 21.11.2019 13:00 - 15:00 D4.0.127
Montag 25.11.2019 13:00 - 15:00 TC.3.06
Montag 02.12.2019 13:00 - 15:00 TC.3.06
Montag 09.12.2019 13:00 - 15:30 TC.3.06

Inhalte der LV

The lecture introduces several fundamental concepts from machine learning as well as deep 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

Standard rules for PIs

Lehr-/Lerndesign

This class is taught as a lecture complemented with exercises.

Leistung(en) für eine Beurteilung

Exercise Series (30%)

Coding Project  (15%)

Final oral exam (55%)

The exercises will be discussed each week.

Literatur

1
Titel:

Autor/in: Josef Teichmann (joint lecture project with Christa Cuchiero, Matteo Gambara, Hanna Wutte)

Titel:

Machine Learning in Finance

https://people.math.ethz.ch/~jteichma/index.php?content=teach_mlf2019


Anmerkungen: This is the basic material. The Jupyter notebooks will be updated during the course
Zuletzt bearbeitet: 06.10.2019



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