| Dozent/in |
Dr. rer. nat. Hanna Bettine Fechner |
| Veranstaltungsart |
Masterseminar |
| Code |
HS261136 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
Gesundheitswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Di, 15.09.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 22.09.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 29.09.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 06.10.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 13.10.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 20.10.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 27.10.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 03.11.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 10.11.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 17.11.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 24.11.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 01.12.2026, 12:30 - 14:00 Uhr, 3.B01
Di, 15.12.2026, 12:30 - 14:00 Uhr, 3.B01 |
| Weitere Daten |
This method-oriented course is equally suitable for students majoring in Health Data Science and students majoring in other subjects (course credited as part of the electives) who wish to use the methods in content-related courses for projects as well as in research internships and/or master's theses.
For the exercises, the students will work on their own laptops on which they have installed the software R, R Studio, and topic-specific R packages. |
| Umfang |
2 Semesterwochenstunden |
| Inhalt |
Statistical learning models are tools for understanding and predicting data. The course gives an overview of supervised and unsupervised learning models for regression and classification problems that have a wide range of applications in health data science. Topics include techniques for training and testing models, model selection and regularization (ridge regression and lasso), illustrated with linear and logistic regression models, k-nearest neighbors, trees and random forests, central elements and principles of neural networks, cluster analysis, and dimension reduction with principal component analysis.
For each modeling technique, there will be a short theoretical introduction, followed by a practical implementation in R und the interpretation of the resulting R output. Complementary exercises will be provided for students to gain hands-on experience with the modeling techniques; students will present their solutions to each other. In the end of the course, students will apply their knowledge by presenting and discussing scientific research papers from various fields of the health sciences (e.g., health economics, politics and sociology, health psychology and psychiatry, and medicine and medical technology) that contextualize the modeling techniques covered. |
| E-Learning |
Course materials are made available or linked on the e-learning platform Moodle, and solutions to the exercises and presentation slides are submitted via Moodle. |
| Lernziele |
Upon completion of the course, students will be able to
• describe the central principles and background of different modeling techniques and explain how they can be applied to data from the health sciences
• implement the modeling techniques using the software R and interpret the results
• read, present, and critically evaluate scientific research papers from the health sciences that used the modeling techniques covered |
| Voraussetzungen |
Knowledge in the areas of data visualization and statistics / quantitative methods (e.g., linear and logistic regression). Experience with the software R and R Studio or the willingness to acquire this knowledge before the start of the course.
Please bring your own laptops with an installation of R and R Studio. |
| Sprache |
Englisch |
| Begrenzung |
The course is limited to 14 participants. The limit will be administered via Moodle according to the chronological order of registration. From 31st August 2026, 12:00 p.m. (noon), it will be possible to register via Moodle. As soon as 14 participants are registered, the registration window will close automatically. If you would like to be put on the waiting list, please send an email to: masterhealth@unilu.ch |
| Anmeldung |
Moodle: https://elearning.hsm-unilu.ch/course/view.php?id=1029
|
| Leistungsnachweis |
Grading will be based on 1) the coding solutions and presentation of these solutions for an exercise sheet in R (50%), and 2) the slides and presentation of a scientific article in which statistical learning models were used (50%). An overall grade of 4.0 or better is required to successfully complete the course.
IMPORTANT: In order to earn credits and participate at the exam registration via Uni Portal within the exam registration period is MANDATORY. Further information: www.unilu.ch/en/study/courses-exams-regulations/health-sciences-and-medicine/exams/ |
| Abschlussform / Credits |
Coding solutions in R and their presentation, slides and presentation of scientific article / 3 Credits
|
| Hinweise |
Teaching methods:
Theoretical inputs, demonstrations, exercises, presentations, group work and discussions by students.
For the exercises, the students will work on their own laptops on which they have installed the software R, R Studio, and topic-specific R packages. |
| Hörer-/innen |
Nein |
| Kontakt |
hanna.fechner@unilu.ch |
| Material |
Course materials are made available or linked on the e-learning platform Moodle. |
| Literatur |
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1 |