Projects in ML Research

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 to reproduce a known result in the specific field.

A tentative list of topics for this year:
- The Role of Data in ML
- Scientific ML in Earth sciences
- The Language of Physics
- Probabilistic ML in precision medicine
- Intelligence augmentation in the built environment
- Social Science as a Problem Space for NLP
- ...

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.