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| Semester | Frühjahrsemester 2023 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende | Marcel Lüthi (marcel.luethi@unibas.ch, BeurteilerIn) |
| Inhalt | Statistical shape models are models, which represent the typical variability of an organ or shape in terms of a probability distribution. The most important application of shape models is in medicine, where they are used for the analysis of medical images, surgery planning or the design of implants. There are, however, also many application in other fields, such as bio-mechanics, anthropology or forensics. In this course the participants will address a classical problem in the forensics sciences using shape modelling: Given a fragment of a bone, what can we say about the person? To solve this mystery, we will combine methods from mathematics, statistics and machine learning. More precisely, the participants will learn about Gaussian processes, Bayesian modelling and Markov Chain Monte Carlo methods. All the mathematical methods will be practically explored and visualized using the open source software library Scalismo. |
| Lernziele | At the end of the course the students should be able to - describe how shapes can be analyzed using statistics . - apply the mathematical concept of a Gaussian process to model anatomical shapes - follow a principled Bayesian workflow for analysing shapes. - develop programs for medical image analysis using the open source software scalismo |
| Literatur | Links to related literature will be given as part of the course. |
| Bemerkungen | Online course "Shape Modelling - Computing the human anatomy" available at https://shapemodelling.cs.unibas.ch/ssm-course/ |
| Weblink | Course Webpage |
| Teilnahmebedingungen | Open to Master and PhD students with basic knowledge in probability theory and statistics, linear algebra as well as programming experience in a modern programming language (e.g. Java or C++) |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | Online-Angebot obligatorisch |
| HörerInnen willkommen |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Dienstag | 14.15-16.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Datum | Zeit | Raum |
|---|---|---|
| Dienstag 21.02.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 28.02.2023 | 14.15-16.00 Uhr | Fasnachstferien |
| Dienstag 07.03.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 14.03.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 21.03.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 28.03.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 04.04.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 11.04.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 18.04.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 25.04.2023 | 14.15-16.00 Uhr | Kollegienhaus, Seminarraum 103 |
| Dienstag 02.05.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 09.05.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 16.05.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 23.05.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Dienstag 30.05.2023 | 14.15-16.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Donnerstag 06.07.2023 | 14.00-16.00 Uhr | Alte Universität, Hörsaal -101 |
| Module |
Doktorat Informatik: Empfehlungen (Promotionsfach: Informatik) Modul: Applications of Distributed Systems (Masterstudium: Computer Science) Modul: Concepts of Machine Intelligence (Master Studienfach: Computer Science) Modul: Methods of Machine Intelligence (Masterstudium: Computer Science) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | The final grade will be computed based on the result of a practical project and a written exam. The project contributes 50% and the written exam 50% to the final grade. Written exam, expected date: 6 July 2023, 2-4, Spiegelgasse 1, room 00.003. |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,5 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Fachbereich Informatik |