CS 707: Data and Web Science Seminar (FSS 2024)

The Data and Web Science seminar covers recent topics in data and web science. This term's seminar is for MSc students only and focuses on selected topics in machine learning.

• This seminar is organized by Prof. Dr. Rainer Gemulla, Adrian Kochsiek, and Simon Forbat.
• Available for Master students (2 SWS, 4 ECTS).
• Prerequisites: solid background in machine learning
• Maximum number of participants is 10 MSc 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 online kickoff meeting on Feb 21, 17:15 (tentative).
• Work individually throughout the semester according to the seminar schedule.
• Meet your advisor for guidance and feedback.

Register via Portal 2 until Feb 12.

If you are accepted into the seminar, provide at least 4 topics of your preference (your own and/or example topics; see below) by Feb 18 via email to Simon Forbat. The actual topic assignment takes place soon afterwards; we will notify you via email. Our goal is to assign one of your preferred topics 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 in the first tutor meeting.

You are generally free to propose your topic area of interest. Your area needs to be (i) an active, relevant area in machine learning, (ii) not part of the MSc Wifo or Data Science, and (iii) not solely refer to a general architecture (such as RNNs, CNNs, Transformers) or a very narrow subfield. If unsure, ask us upfront.

Some examples of suitable topic areas include:

• pretraining
• optimization for machine learning
• machine learning frameworks
• parallel machine learning
• federated machine learning
• differential privacy in machine learning
• regularization
• few-shot learning
• semi-supervised learning
• reinforcement learning
• (deep) generative modelling
• AutoML
• PAC learning
• multi-task learning
• transfer learning
• weak supervision
• ML in a concrete application area
• explainable machine learning
• fair machine learning

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