SM445/CS 707: Seminar “Machine Learning and Time” (FSS 2025)

This term's seminar: Machine Learning and Time.

Time plays a crucial role in virtually all aspects of machine learning. This seminar focuses on recent research in relevant areas where machine learning meets time, including time series analysis, temporal knowledge representation, causality and time, online and data stream learning, forecasting, change point detection, and handling distribution shifts.

• This seminar is organized by Prof. Dr. Rainer Gemulla, Simon Forbat, and Julie Naegelen.
• Available for up to 8 Master students (4 ECTS) and up to 4 Bachelor students (5 ECTS).
• Prerequisites: Solid background in machine learning (MSc students), Einführung in Data Science (BSc students)

In this seminar, you will

• Read, understand, and explore scientific literature
• Summarize a current research topic in a concise report (10 single-column pages + references)
• Give two presentations about your topic (3 minutes flash presentation, 15 minutes final presentation)
• Moderate a scientific discussion about the topic of one of your fellow students
• Review a (draft of a) report of a fellow student

• Register as described below.
• Attend the kickoff meeting on TBD.
• Work individually throughout the semester according to the seminar schedule (TBD).
• Meet your advisor for guidance and feedback.

Please register via Portal 2 until TBD.

If you are accepted into the seminar, provide at least 4 topics areas of your preference (your own and / or example topics; see below) by TBD via email to TBD. The actual topic assignment takes place soon afterwards; we will notify you via email. Our goal is to assign one of your preferred topic areas to you.

You will be assigned a topic area in an active, relevant field of machine learning based your preferences. Your goals in this seminar are

1. Provide a short, concise overview of this topic area (1/4).  A good starting point may be a book chapter, survey paper, or recent research paper. Here you take a birds-eyes view and are expected to discuss the main goals, challenges, and relevance of your topic area. Topic areas are selected at the beginning of the seminar.
2. Present a self-selected topic within this area in more detail (3/4). A good starting point is a recent or highly-influential research paper. Here you dive deep into one particular topic and are expected to discuss and explain the concrete problem statement, concrete solution or contribution, as well as your own thoughts. The actual topic is selected before the first tutor meeting.

You are generally free to propose your topic area of interest as long as it aligns with the overall theme and objectives of the seminar.

Suggested topics TBD.

• “Giving Conference Talks”by Prof. Dr. Rainer Gemulla
• “Writing for Computer Science” by Justin Zobel, Springer, 2014