Machine Learning, Data and Networks
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.
Suggested Topics
Example topics of interest are, but not limited to, the following areas:
Autonomous and robust extraction of information from the web and natural language text (NLP)
Data integration and data cleaning
Deep learning
Explainable AI
Evolutionary algorithms
Mining Web media and social networks
Online search engines and recommender algorithms
One-shot learning, transfer learning, multi-task learning
Process mining
Reinforcement learning and multi-agent systems
Scalable Neural Networks
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: Elena Mocanu and Nicola Strisciuglio