Advanced topics in machine learning

Machine learning is in the centre of research of artificial intelligence. Many researchers worldwide are dealing with the topics related to machine learning, both in academia and industry. This very dynamic field is characterized with fast transfer of solutions into practical use.

The topics in this domain are defined by premier scientific conferences, where top-class researchers meet, for example ICML (International Conference on Machine Learning), NeurIPS (Advances in Neural Information Processing Systems), IJCAI (International Joint Conference on AI), COLT (Conference on Learning Theory).

This thesis will be advised by an external mentor, who will also define its particular topic.

Interesting research challenges are contained within (but are not limited to) these topics:

• General Machine Learning (e.g., active learning, clustering, online learning, ranking, reinforcement learning, semi-supervised learning, unsupervised learning)
• Deep Learning (e.g., architectures, generative models, deep reinforcement learning)
• Learning Theory (e.g., bandits, statistical learning theory)
• Optimization (e.g., matrix/tensor methods, sparsity)
• Trustworthy Machine Learning (e.g., fairness, robustness)

There are many application domains, where advanced machine learning methods can be deployed.

The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.