In this seminar, we study novel algorithms that learn from data streams.
Traditional machine learning algorithms are rarely applicable in scenarios with streaming data. Most algorithms were designed for offline settings, i.e., the entire data set needs to be scanned and processed (multiple times), before a decision can be made.
In this seminar, students will implement, evaluate (and at best improve) machine learning algorithms for data streams from current research projects. We will look at algorithms for classification, regression, clustering, pattern mining, outlier detection, trend detection and recommender systems.
Each team, consisting of two students, chooses and presents a challenging research task and implements the proposed solution as research prototype using the streaming framework Apache Kafka with Kafka Streams.
This is a project seminar: There will be a few weekly lectures including an introductory lecture and an invited talk from industry about Stream Processing with Apache Kafka. Teams will frequently meet with the supervisor.
