With more and more data available for analysis and decision making - from web documents and digital media to sensory data from cameras, microphones, and ubiquitous devices - it becomes increasingly more important to understand how such large volumes of data can be analyzed by computers and used as the basis for new intelligent services, for decision making, and for making computers learn from experience. In companies around the world, from retail and banks all the way to Google, intelligent learning and analysis techniques are used to improve business decisions. Likewise, in science, important discoveries are made easier by automated learning methods, and games and other artifacts are being made adaptive with learning technology.

Within the intelligent systems track of the computer science Master's program, the Algorithms for Data Science course offers in-depth knowledge of different aspects of big data analytics and systems, including algorithmic techniques for analyzing structured and unstructured data that cannot be stored in a single computer because it has enormous size and/or continuously arrives with such a high rate that requires immediate processing. In particular, the topics include classical data mining tasks for massive data and/or data streams, mining massive graphs, and similarity search in massive data.

In-context learning, amortized inference, foundation models, zero-shot models and prior fitted networks. These are different labels of the same machine learning paradigm: pre-trained deep neural networks that may be applied in a range of applications, without any further training. LLMs are the most prominent examples of in-context models.

In this lab, we offer several different projects on in-context learning in non-language domains. Concretely, these are project on inference of stochastic processes, function estimation and time series imputation. Their aim is either to understand and extend, or to develop novel in in-context approaches.

All projects include a review of relevant literature and implementations of these ideas. Experience with deep learning and working knowledge of PyTorch or Jax is therefore required. You will report your progress in a series of presentations and a concluding written lab report. 

We will introduce the projects in the preliminary meeting. Topics will be distributed based on preference and interest.