The recent surge of machine learning (ML) has opened up various opportunities when analyzing big datasets. Beyond basic, non-ML supported techniques of big data analytics, such as identifying similar items in big datasets, or arranging how to distribute jobs on large compute clusters, for example, the ML supported techniques enable to extract knowledge from large datasets at utmost diversity and accuracy.

The seminar will start with a mini lecture. First, lectures will explain how to cluster datasets. Clustering is an 'unsupervised' machine learning technique by which to mine social network graphs, for example. Second, 'supervised' machine learning techniques (where 'deep learning' likely is the most prominent recent technique) and their use in analyzing big data will be discussed. The mini lecture will be followed by seminar presentations, to be presented in small groups of 2-3 students.

General Reading
Papers