Rima Alaifari: Catalogue data in Autumn Semester 2022 |
| Name | Prof. Dr. Rima Alaifari |
| Field | Applied Mathematics |
| Address | Seminar für Angewandte Mathematik ETH Zürich, HG G 59.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 32 00 |
| rima.alaifari@sam.math.ethz.ch | |
| URL | http://www.sam.math.ethz.ch/~rimaa |
| Department | Mathematics |
| Relationship | Assistant Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 401-4661-DRL | Robustness of Deep Neural Networks  Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to info@zgsm.ch with their name, course number and student ID. Please see https://zgsm.math.uzh.ch/index.php?id=forum0 | 2 credits | 2G + 1A | R. Alaifari | |
| Abstract | While deep neural networks have been very successfully employed in classification problems, their stability properties remain still unclear. In particular, the presence of adversarial examples has demonstrated that state-of-the-art networks are vulnerable to small perturbations in the data. This course serves as an introduction to adversarial attacks and defenses for deep neural nework algorithms. | ||||
| Objective | 1. Theory: in this course, we will discuss the trade-off between accuracy and stability of classification algorithms and study the state-of-the-art for robust image classification, adversarial attacks and adversarial training. 2. Practice: students will train and attack deep neural networks themselves, to get a hands-on experience. | ||||
| Prerequisites / Notice | Courses on linear algebra, optimization and machine learning. Basic programming skills in Python, and experience with PyTorch or TensorFlow. | ||||
| 401-4661-72L | Robustness of Deep Neural Networks | 6 credits | 2G + 1A | R. Alaifari | |
| Abstract | While deep neural networks have been very successfully employed in classification problems, their stability properties remain still unclear. In particular, the presence of adversarial examples has demonstrated that state-of-the-art networks are vulnerable to small perturbations in the data. This course serves as an introduction to adversarial attacks and defenses for deep neural nework algorithms. | ||||
| Objective | 1. Theory: in this course, we will discuss the trade-off between accuracy and stability of classification algorithms and study the state-of-the-art for robust image classification, adversarial attacks and adversarial training. 2. Practice: students will train and attack deep neural networks themselves, to get a hands-on experience. | ||||
| Prerequisites / Notice | Courses on linear algebra, optimization and machine learning. Basic programming skills in Python and experience with PyTorch. | ||||
| 401-5650-00L | Zurich Colloquium in Applied and Computational Mathematics  | 0 credits | 1K | R. Abgrall, R. Alaifari, H. Ammari, R. Hiptmair, S. Mishra, S. Sauter, C. Schwab | |
| Abstract | Research colloquium | ||||
| Objective | |||||