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