Faculty
Mario Fritz ist leitender Wissenschaftler am CISPA Helmholtz Zentrum für Informationssicherheit, Honorarprofessor an der Universität des Saarlandes und Fellow des European Laboratory for Learning and Intelligent Systems (ELLIS). Bis 2018 leitete er eine Forschungsgruppe am Max Planck Institute für Informatik. Zuvor war er PostDoc am International Computer Science Institute (ICSI) und an der UC Berkeley nach Promotion an der TU Darmstadt und Informatikstudium an der FAU Erlangen-Nürnberg. Seine Forschung fokussiert sich auf vertrauenswürdige künstliche Intelligenz insbesondere an der Schnittmenge von Informationssicherheit und maschinellem Lernen. Er ist Associate Editor der Zeitschrift "IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)", koordiniert das Helmholtz Projekt "Trustworthy Federated Data Analytics" und hat über 100 wissenschaftliche Artikel publiziert - 80 davon in den Top-Konferenzen und Zeitschriften.
USENIX
32nd USENIX Security Symposium32nd USENIX Security Symposium
USENIX-Security
USENIX SecurityUSENIX Security
NeurIPS
Proceedings of Machine Learning ResearchThirty-sixth Conference on Neural Information Processing Systems
USENIX-Security
USENIX Security Symposium (USENIX Security)USENIX Security Symposium (USENIX Security), 2022.
ICML
Proceedings of the 39th International Conference on Machine LearningInternational Conference on Machine Learning (ICML)
CVPR
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference on Computer Vision and Pattern Recognition
CVPR
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference on Computer Vision and Pattern Recognition
ICLR
International Conference on Learning RepresentationsThe Tenth International Conference on Learning Representations
ICLR
International Conference on Learning RepresentationsThe Tenth International Conference on Learning Representations
UNSPECIFIED
Proceedings of the Conference on Health, Inference, and LearningConference on Health, Inference, and Learning
Trustworthy Machine Learning
Machine learning has made great advances over the past year and many techniques have found their ways into applications. This leads to an increasing demand of techniques that not only perform well - but are also "trustworthy".
Trustworthiness includes:
- Interpretability of the prediction
- Robustness against changes to the input, which occur naturally or with malicious intend
- Privacy preserving machine learning (e.g. when dealing with sensitive data such as in health applications)
- Fairness
- ...
As a proseminar’s primary purpose is to learn presentation skills, the seminar will feature two presentations from each student.
In the first half of the semester, we will have presentations of two topics each week. After each presentation, fellow students and lecturers will provide feedback on how to improve the presentation. This general feedback must then be taken into account for the second half of the semester, where again each student will present.
The first presentations will count towards 30% of the overall grade, the second presentation will count towards 70% of the overall grade. Attendance in the proseminar meetings is mandatory. At most one session can be skipped, after that you need to bring a doctor’s note to excuse your absence.
Machine Learning in Cyber Security
Recent advances in Machine Learning has lead to near (or beyond) human-level performance in many tasks - autonomous driving, voice assistance, playing a variety of games. In terms of privacy and security, this is a double-edged sword. ML techniques can be used to efficiently detect and prevent attacks (e.g., intrusion detection). However, their deployment to many real-world sensitive systems (e.g., self-driving cars, the cloud) also makes them susceptible to numerous attacks, such as introducing imperceptible perturbations in inputs and forcing ML systems behave in unintended ways.
The course explores in-depth both of these sides to Machine Learning and Cyber Security. The content addresses the following areas:
While we do a brief recap in the beginning, the course requires knowledge on Machine Learning.
Date for lecture: Tuesdays noon to 2pm.
Date for exercise: Fridays 2pm to 4pm
Due to the size of the course - the lecture will start in an online format until further notice.
The course requires prior knowledge on Machine Learning.
Once you have registered - please find internal information and schedule and links here (under construction).
Proseminar: Trustworthy Machine Learning
Machine learning has made great advances over the past year and many techniques have found their ways into applications. This leads to an increasing demand of techniques that not only perform well - but are also "trustworthy".
Lecture: High Level Computer Vision
This course will cover essential techniques for high-level computer vision. These techniques facilitate semantic interpretation of visual data, as it is required for a broad range of applications like robotics, driver assistance, multi-media retrieval, surveillance etc.
Lecture: Machine Learning in Cybersecurity
Recent advances in Machine Learning has lead to near (or beyond) human-level performance in many tasks - autonomous driving, voice assistance, playing a variety of games.