Data science

Introduction

In a world where we are more and more connected, data is everywhere. You can extract knowledge and provide solutions benefiting society in a large variety of domains, including health, engineering, safety and security, business, and science. You can design or extend methods to mine, analyze, classify, or regress from data to solve a research question of your choice. This method may be supervised, unsupervised or reinforcement learning. Concepts such as fairness, data quality, trust, and safety may also be addressed.

Topics

Here is a list of current research questions, for inspiration:

Data Science and AI

  • Explainable AI

  • Mining Web media and social networks

  • Online search engines and recommender algorithms

  • Autonomous and robust extraction of information from the web and natural language text

  • Data integration and data cleaning

  • Evolutionary algorithms

  • Process mining

Machine learning

  • One-shot learning

  • Scalable Deep Learning

  • (Deep) reinforcement learning

  • Applications on: deep learning, reinforcement learning, transfer learning, multi-task learning

Further reading

A list of example project descriptions for this track: https://www.utwente.nl/en/eemcs/ds/assignments/open/bachelor/

Information

For further information on the content of this track, you may contact the track chairs: Doina Bucur and Elena Mocanu