Incremental Learning from Data Streams

The course aims to teach students about state-of-the-art algorithms used to perform learning from data streams. The course will guide the students through the major open challenges in the field (supervised learning, data compression, concept drift detection, clustering from streams, specialized evaluation statistics). With this knowledge, the students will be able to apply their machine learning skills to a specialized and useful area connected to the abundance of data in our everyday lives (bank/weather/financial transactions, sensor readings, etc.). The course will be organized by mixing lectures with hands-on lab exercises in the Statistical package R. The students will apply the acquired knowledge to their problems. The course will stimulate competition between students to achieve the best possible learning results.

Restrictions/Prerequisites: Basic concepts of supervised and unsupervised learning in machine learning/data mining. Basic mathematical and statistical knowledge is also welcome for easier understanding of data sampling and descriptor techniques. It is mandatory that the students have programmed before in any procedural or object-oriented language as the practicals and tasks will be held using Statistical package R. Having R installed at their own computers (notebooks) is desired.