Interpretable ML
Kursbeschreibung
| Studiengang | Modulkürzel | Leistungs- bewertung |
|---|---|---|
| BA-2010[100%|75%] | CS-CL | 6 LP |
| BA-2010[50%] | BS-CL | 6 LP |
| BA-2010[25%] | BS-AC | 4 LP |
| BA-2010 | AS-CL | 8 LP |
| Master | SS-CL, SS-TAC | 8 LP |
| Dozenten/-innen | Michael Staniek |
| Veranstaltungsart |
|
| Sprache | English |
| Erster Termin | 17.10.2022 16:00, INF 327 / SR 3 |
| Zeit und Ort | Montags, 15:15-16:45, INF 327 / SR 3 |
| Commitment-Frist | tbd. |
Leistungsnachweis
- Presentation
- Project
Inhalt
Compared to machine learning models like decision trees, neural networks on their own can not be interpreted. This leads many people to be sceptic about neural networks.
The whole field of Interpretable Machine Learning wants to raise confidence in machine learning by allowing to look into the decision making process of neural networks.
