High Performance EM for Gaussian Mixture
- Forschungsthema/Bereich
- Artificial Intelligence
- Typ der Abschlussarbeit
- Bachelor / Master
- Startzeitpunkt
- -
- Bewerbungsschluss
- 30.06.2028
- Dauer der Arbeit
- 4 months(BSc) - 6 months(MSc)
Beschreibung
High performance Expectation Maximization (EM) for Gaussian Mixture is compelling because it unlocks scalable, accurate density estimation for modern datasets. Faster E and M steps enable real time clustering, anomaly detection, and soft classificationin streaming and interactive applications. Optimized linear algebra, vectorization, and GPU acceleration reduce runtime and energy, broadening feasibility on edge and cloud. Careful numerical stability, batching, and memory layout improve convergence
and robustness on large datasets. Parallelized responsibilities, batched covariance updates, and efficient mixture normalization increase throughput without sacrificing precision. Such implementations empower rapid model selection, online updates, and hyperparameter sweeps, driving better decisions in diverse range of applicatioons.
Voraussetzung
- Voraussetzungen an Studierende
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- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Studiengangsbereiche
-
- Ingenieurwissenschaften
Informatik
- Ingenieurwissenschaften
Betreuung
- Titel, Vorname, Name
- Ali Darijani
- Organisationseinheit
- Computer Science(IAR/IES)
- E-Mail Adresse
- ali.darijani@iosb.fraunhofer.de
- Link zur eigenen Homepage/Personenseite
- Website
Bewerbung per E-Mail
- Bewerbungsunterlagen
-
E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
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