401-3915-73L  Machine Learning in Finance and Insurance

SemesterAutumn Semester 2023
LecturersP. Cheridito
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-3915-73 VMachine Learning in Finance and Insurance2 hrs
Tue16:15-18:00HG D 7.1 »
P. Cheridito
401-3915-73 UMachine Learning in Finance and Insurance1 hrs
Wed16:15-17:00HG D 1.1 »
P. Cheridito

Catalogue data

AbstractThis course introduces machine learning methods that can be used in finance and insurance applications.
Learning objectiveThe goal is to learn methods from machine learning that can be used in financial and insurance applications.
ContentLinear, polynomial, logistic, ridge and lasso regression, dimension reduction methods, singular value decomposition, kernel methods, support vector machines, classification and regression trees, random forests, XGBoost, neural networks, stochastic gradient descent, autoencoders, graph neural networks, transfomers, credit analytics, pricing, hedging, insurance claim prediction.
Lecture notesCourse material is available on https://people.math.ethz.ch/~patrickc/mlfi
LiteratureMatthew F. Dixon, Igor Halperin, Paul Bilokon (2020). Machine Learning in Finance. Springer.

Ian Goodfellow, Yoshua Bengio and Aaron Courville (2020). Deep Learning. MIT Press.

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2021). An Introduction to Statistical Learning. Springer.

Marcos Lopez de Prado (2018). Advances in Financial Machine Learning. Wiley.

Marcos Lopez de Prado (2020). Machine Learning for Asset Managers. Cambridge Elements.

Mario V. Wüthrich and Michael Merz (2023). Statistical Foundations of Actuarial Learning and its Applications. Springer.
Prerequisites / NoticeThe course requires basic knowledge in analysis, linear algebra, probability theory and statistics.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Project Managementassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Leadership and Responsibilityassessed
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-direction and Self-management assessed

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersP. Cheridito
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationPractical projects are an integral part of the course. Participation is mandatory.

The grade for the course will be calculated as a weighted average of the grade achieved in the final exam (70%) and the grade achieved in the practical projects (30%).
Written aids10 single-sided A4 pages of notes. No books or lecture notes. Laptops, tablets and mobile phones must be switched off.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkCourse website
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Data Science MasterInterdisciplinary ElectivesWInformation
Mathematics MasterSelection: Financial and Insurance MathematicsWInformation
Computational Science and Engineering MasterComputational FinanceWInformation