401-3932-19L  Machine Learning in Finance

SemesterSpring Semester 2021
LecturersJ. Teichmann
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-3932-19 VMachine Learning in Finance3 hrs
Mon10:15-12:00ML F 36 »
Wed11:15-12:00LFW C 5 »
J. Teichmann
401-3932-19 UMachine Learning in Finance1 hrs
Wed10:15-11:00LFW C 5 »
J. Teichmann

Catalogue data

AbstractThe course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deep
networks and wavelet analysis, Deep Hedging, Deep calibration,
Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversersial networks, Economic games.
Objective
Prerequisites / NoticeBachelor in mathematics, physics, economics or computer science.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersJ. Teichmann
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 90 minutes
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Doctoral Department of MathematicsGraduate SchoolWInformation
Mathematics MasterSelection: Financial and Insurance MathematicsWInformation
Quantitative Finance MasterMathematical Methods for FinanceWInformation
Computational Science and Engineering MasterComputational FinanceWInformation