Algorithmic Foundations of Data Science
In the age of "big data" and "advanced analytics", data processing faces new challenges. Queries become more complex and often involve data mining and machine learning tasks, and the scale of the datasets requires new algorithmic approaches.
This course will cover the "theoretical foundations" of modern data processing and analytics. This includes topics from database theory, such as data models, the analysis of query languages, and basic algorithmic and complexity theoretic questions related to query processing. It also includes topics from algorithmic learning theory, such as basic machine learning algorithms, support vector machines, the PAC model, and VC-Dimension. Furthermore, it includes new models of computation on massive datasets, such as the streaming model and the map-reduce paradigm, and algorithms for these models.
We will focus on "computational aspects" of the theory. Statistics, though undoubtedly one of the foundations of data science, will not play a central role in this course.
There are no prerequisites required.
All information of this lecture can be found in RWTHmoodle
The course will be held in english.