• Course code:63834
  • Credits:5
  • Semester: winter
  • Contents

The goal of the proposed course is to teach the students about the state-of-the-art algorithms that are 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 that is 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 that the students will do in the Statistical package R. The lab exercises will include applying the acquired knowledge on their own problem and stimulating a competition between students to achieve the best possible learning results.

  • Study programmes
  • Distribution of hours per semester
15
hours
lectures
15
hours
laboratory work
20
hours
tutorials
  • Professor
Course Organiser
Room:R2.07