Learning Systems are systems that use experience to construct a general model and to improve their performance. Learning methods are used in a variety of systems including:
* systems for data mining, * text and image classification, * recognition of objects and information in texts, * data mining, * robot control.
Emphasis in this track is on algorithms, models for learning, theories that explain why algorithms work (Bayesian statistics, Reinforcement Learning and Minimal Description Length), multi-agent reinforcement learning and transfer learning for multiple modalities. The track is associated with the Autonomous Intelligent Systems group and the Adaptive Information Management group but freely collaborates with others.
You can easily combine this track with other areas in Artificial Intelligence and combine Learning with topics like computer vision, language learning, autonomous systems, web mining or user modelling. For the more theoretically inclined students there are possibilities to go deeper into the formal approaches to learning. Although the focus is on research, the track provides a solid basis for further work in industry.
Besides the core curriculum the following courses are offered in the Learning Systems track:
* Autonomous Agents and Multi-Agent Systems (track course) * Machine Learning: Principles and Methods (track course) * Web Text Mining * Unsupervised Language Learning * Statistical Structure in Language Processing * Advanced Database Systems
Minimum bachelor degree
Recipient: University of Amsterdam
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