Lab - Machine Learning

The lab aims to provide practical experience with methods and algorithms in Artificial Intelligence (AI) and Machine Learning (ML) through a two-trimester-long team project.

Students select a project from a list of offered topics. The projects are typically divided into two categories:

• Academic projects, which are usually based on published research work. The goal is to reproduce, analyze, and extend existing approaches, potentially applying them to new datasets or problem settings.
• Application-oriented projects, which focus on the analysis of interesting real-world datasets from different application domains.

Throughout the lab, students will develop practical skills in implementing AI/ML pipelines, conducting experiments, analyzing results, and organizing project workflows.

At the end of the trimester, students are expected to submit well-documented code (e.g., Python notebooks) together with a short written report. Project results are presented in a final presentation session followed by discussion and Q&A.

Learning objectives. By the end of the lab, students will be able to:

• Apply AI/ML methods to practical problems
• Implement and evaluate AI/ML workflows
• Work collaboratively on a team-based AI/ML project
• Analyze experimental results and model performance
• Document and present technical work in a structured manner

Project types:

• Research reproduction and extension
• Application-oriented data analysis
• Machine learning experimentation
• Model evaluation and benchmarking