Menna El-Assady: Catalogue data in Spring Semester 2023 |
| Name | Prof. Dr. Menna El-Assady |
| Name variants | Mennatallah El-Assady |
| Field | Computer Science |
| Address | Institut für Visual Computing ETH Zürich, STF F 112 Stampfenbachstrasse 114 8092 Zürich SWITZERLAND |
| menna.elassady@ai.ethz.ch | |
| URL | https://el-assady.com/ |
| Department | Computer Science |
| Relationship | Assistant Professor (Tenure Track) |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 263-5051-00L | AI Center Projects in Machine Learning Research   Last cancellation/deregistration date for this ungraded semester performance: Friday, 17 March 2023! Please note that after that date no deregistration will be accepted and the course will be considered as "fail". | 4 credits | 2V + 1A | A. Ilic, N. Davoudi, M. El-Assady, F. Engelmann, S. Gashi, T. Kontogianni, A. Marx, B. Moseley, G. Ramponi, X. Shen, M. Sorbaro Sindaci | |
| Abstract | The course will give students an overview of selected topics in advanced machine learning that are currently subjects of active research. The course concludes with a final project. | ||||
| Learning objective | The overall objective is to give students a concrete idea of what working in contemporary machine learning research is like and inform them about current research performed at ETH. In this course, students will be able to get an overview of current research topics in different specialized areas. In the final project, students will be able to build experience in practical aspects of machine learning research, including research literature, aspects of implementation, and reproducibility challenges. | ||||
| Content | The course will be structured as sections taught by different postdocs specialized in the relevant fields. Each section will showcase an advanced research topic in machine learning, first introducing it and motivating it in the context of current technological or scientific advancement, then providing practical applications that students can experiment with, ideally with the aim of reproducing a known result in the specific field. A tentative list of topics for this year: - fully supervised 3D scene understanding - weakly supervised 3D scene understanding - causal discovery - biological and artificial neural networks - reinforcement learning - visual text analytics - human-centered AI - representation learning. The last weeks of the course will be reserved for the implementation of the final project. The students will be assigned group projects in one of the presented areas, based on their preferences. The outcomes will be made into a scientific poster and students will be asked to present the projects to the other groups in a joint poster session. | ||||
| Prerequisites / Notice | Participants should have basic knowledge about machine learning and statistics (e.g. Introduction to Machine Learning course or equivalent) and programming. | ||||
| 263-5052-00L | Interactive Machine Learning: Visualization & Explainability | 5 credits | 3G + 1A | M. El-Assady | |
| Abstract | Visual Analytics supports the design of human-in-the-loop interfaces that enable human-machine collaboration. In this course, will go through the fundamentals of designing interactive visualizations, later applying them to explain and interact with machine leaning models. | ||||
| Learning objective | The goal of the course is to introduce techniques for interactive information visualization and to apply these on understanding, diagnosing, and refining machine learning models. | ||||
| Content | Interactive, mixed-initiative machine learning promises to combine the efficiency of automation with the effectiveness of humans for a collaborative decision-making and problem-solving process. This can be facilitated through co-adaptive visual interfaces. This course will first introduce the foundations of information visualization design based on data charecteristics, e.g., high-dimensional, geo-spatial, relational, temporal, and textual data. Second, we will discuss interaction techniques and explanation strategies to enable explainable machine learning with the tasks of understanding, diagnosing, and refining machine learning models. Tentative list of topics: 1. Visualization and Perception 2. Interaction and Explanation 3. Systems Overview | ||||
| Lecture notes | Course material will be provided in form of slides. | ||||
| Literature | Will be provided during the course. | ||||
| Prerequisites / Notice | Basic understanding of machine learning as taught at the Bachelor's level. | ||||