This course includes a mock exam, which is designed to help students get used to the new exam form of "Take-home exam" (Schriftliche Ausarbeitung), which will be used for several courses of the AG KMD in the winter semester 2020/21, including DM II, RecSys and ITO.
Target audience: Students who are taking a course of the AG KMD in the winter semester 2020/21.

Dieser Kurs enthält eine Probeklausur, die dazu dient, dass sich Studierende an die neue Prüfungsform der "Schriftlichen Ausarbeitung" gewöhnen können, die im Wintersemester 2020/21 für mehrere Veranstaltungen der AG KMD verwendet wird, zum Beispiel DM II, RecSys und ITO.
Zielgruppe: Studierende, die eine Lehrveranstaltung der AG KMD im Wintersemester 2020/21 belegen.
Diese Veranstaltung befasst sich mit der Rolle der Informationstechnologie in Organisationen. Es werden Grundzüge der Informationsysteme in einem modernen Unternehmen besprochen.

In this course, we discuss advanced Data Mining methods for Data Science:

  • Dealing with VELOCITY: methods for supervised, semi-supervised and unsupervised learning on data streams
  • Dealing with VOLATILITY: learning and adaption on dynamic data
  • Dealing with VOLUME: methods for learning on high-dimensional data
  • VERACITY: incorporating expert knowledge into the learning process

From the applications' perspective, we focus on web applications and on applications from the domain of medical research.

Goal of this course is to make you familiar with recommenders. You will learn what requirements are placed to a recommender by the business operating it and by the users interacting with it, and you will become proficient in methods used to meet these requirements.

A recommender has a front-end service responsible for the interaction, and a back-end machine learning core that derives the recommendations to be presented to the users and learns from the users' behavior. Most part of the course is on the back-end: you will become familiar with basic and advanced methods that model the recommendation task as an optimization problem and deliver solutions for it.

A recommender learns from information on the items to be recommended and the users to be served. You will see methods that extract such information from data – mainly opinionated texts.