Giorgia Ramponi: Catalogue data in Spring Semester 2023

Name	Dr. Giorgia Ramponi
Address	ETH AI Center ETH Zürich, OAT X 14 Andreasstrasse 5 8092 Zürich SWITZERLAND
E-mail	giorgia.ramponi@ai.ethz.ch
Department	Computer Science
Relationship	Lecturer

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.