Teaching

CS4145 Crowd Computing

4th quarter of the 2021-2022 semester
Crowd computing studies how large groups of people can solve complex tasks that are currently beyond the capabilities of artificial intelligence algorithms, and that cannot be solved by a single person alone. The objective of the Crowd Computing course is to introduce the scientific and technical underpinnings of crowd computing, and to investigate how it can be used for computer science applications (e.g., information retrieval, machine learning, next-generation interfaces, and data mining) and for real world applications (e.g., cultural heritage preservation, online knowledge creation, smart cities, etc.)

IN4325 Information Retrieval

3rd quarters of the 2021-2022 semester
Information Retrieval (IR) is the discipline that deals with the representation, storage, organisation of, and access to information items, and it is concerned with providing efficient access to large amounts of unstructured contents, such as text, images, videos etc. The objective of the IN4325 - Information Retrieval course is to introduce the scientific underpinnings of the field of Information Retrieval. The course aims at providing students basic information retrieval concepts and more advanced techniques for efficient data processing, storage, and querying.

IN4252 Web Science & Engineering

1st and 2nd quarters of the 2021-2022 semester
The main subject of the course is the Web, and in particular Web Data. The course considers developments in the Web and the (big) data management challenges associated to it. In particular, the course considers the relationship between people and technology that come with the Web and Web-based information systems. The course considers the Web both from an engineering perspective as well as from an analytical perspective.

CS4305TU Applied Machine Learning

1st quarter of the 2021-2022 semester
Machine Learning is increasingly important to fields outside of traditional Artificial Intelligence and Computer Science, proving a powerful technique to study data from different domains. This course aims to give students from different technical backgrounds a better understanding of a range of machine learning techniques. During the course, the focus lies on understanding how to use these different techniques, rather than on trying to improve the techniques themselves. To do this, this course will focus on demonstrating how machine learning can be used in different domains and for different types of data.

Master Theses

[2022] Shreyan Biswas. Philosophy Grounded Explainable AI.

[2022] Anitej Palakodeti. Declarative Image Generation.

[2022] Meng Zheng. Debugging Machine Learning on Time-series Data.

[2022] Philippe Lammerts. Maximizing the utility of Machine Learning models with rejection.

[2022] Quentin Lee. Chatbot processing customer complaints for KWR. In collaboration with KWR, Water Research Institute.

[2022] Dina Chen. Human-Centered NLP for Anomaly Detection in Financial Data. In collaboration with ING.

[2022] Xinyue Chen. Safeguarding Machine Vision in Cities: Know What Your Machine Shouldn’t Know. In collaboration with AMS, Amsterdam Institute for Metropolitan Solutions.

[2022] Siwei Wang. Bridging the Gap Between Language Models and Knowledge Bases. In collaboration with KB, the National Library of The Netherlands.

[2022] Wenhui Wang. Detecting Data Drift with and for High-Confidence Errors. In collaboration with ILT, Ministry of Water and Infrastructure Management.

[2022] Zhen Wang. Benchmarking Causal Question Answering.

[2022] Bart Ziengs. A Human-In-the-Loop System for Interpreting Image Recognition Models.

[2021] Pavel Hoogland. Characterizing and Mitigating High-confidence Errors with Humans In the Loop. In collaboration with ILT, Ministry of Water and Infrastructure Management.

[2021] Django E.R. Beek. An Agent-based Opinion Dynamics Model with a Language Model-based Belief System: Bridging Language and Opinion Modeling.

[2021] D.J. van der Werf. One Step Ahead: A Weakly-supervised Approach to Training Robust Machine Learning Models for Transaction Monitoring. In collaboration with Bunq.

[2021] Ashay Somai. Characterizing AI Weaknesses in Detecting Personal Data from Images by Crowds.

[2021] Hao Liu. LightDigit: Embedded Deep Learning Empowered Fingertip Air-Writing with Ambient Light.

[2021] Arjo van Ramshorst. RACE:GP – a Generic Approach to Automatically Creating and Evaluating Hybrid Recommender Systems.

[2021] Shipra Sharma. Clustering Small and Medium Sized Dutch Enterprises Using Hybrid Intelligence. In collaboration with Exact.