Kategorie: ‘Courses’
Web Science
More than thirty years after the birth of the World Wide Web, Web Science is an established study field in Computer Science. This course introduces fundamental concepts (web centralities & basic algorithms, network models and web engineering principles) of Web Science. We will learn fundamental algorithms for web page ranking like PageRank and HITS as well as community detection algorithms. In the engineering part we dig into scalable approaches like cloud computing and peer-to-peer partly based on Post-HTTP protocols like the XMPP and WebRTC are. We will learn about Web Services and their RESTful implementation. With the knowledge gained in the preceding chapters we can analyze and engineer advanced Web applications.
Privacy Enhancing Technologies for Data Science
This lecture covers current research results in the area of Privacy Enhancing Technologies (PETs) which can be applied to Data Science. These PETs have the potential to enable a new generation of privacy-enabled services which are not focused on maximizing the collection of user data. We use a mix of recent book chapters and papers from conferences and journals of the last few years as primary source material.
Bridge Course Databases
A blended learning bridge course for master students in Data Science, Computational Social Science and related programs.
Social Computing Seminar
Social Computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. In this seminar we explore recent topics in social computing like Social Bots, Fake News, Filter Bubbles, Socio-political campaigns, Shit & Candy Storms, Social Augmented and Virtual Reality, Gamification, Serious Games, Science 2.0
Data Science in Medicine
Health data analytics is one of the main drivers for the future of medicine. Various sources of big data, including patient records, diagnostic images, genomic data, wearable sensors, are being generated in our everyday life by health care practitioners, researchers, and patients themselves. Data science aims to identify patterns, discovering the underlying cause of diseases and well being by analyzing this data.
Data Stream Management and Analysis
In many fields today data is produced continuously, potentially unbounded, and at high rates, which is termed as data stream. Applications in smart manufacturing, aerospace, particle physics, or stock exchange trading have a high demand to handle and analyze the massive data streams created. Due to their challenging characteristics specific technologies and methods for data management and analysis have been developed. In this course, you will get a deep understanding of these principles and techniques, such as query processing and optimization or data stream mining.
Mixed Reality Lab
Mixed Reality is a continuum of spatial computing experiences on virtual, augmented and extended reality devices, such as the Microsoft HoloLens, the HTC Vive, and mobile phones. In this lab, we learn the basics of mixed reality software development in hands-on lessons with practical tasks. The lab contains a small independent project student groups can propose and work on.
Algorithmen für die Entdeckung von Communities in sozialen Netzwerken
In diesem Proseminar werden sogenannte Overlapping Community Detection Algorithms (OCDA) mittels eines multi-perspektivischen Kriterienkatalogs untersucht. Neben klassischen informatischen Kriterien wie Korrektheit, Laufzeit und Speicherplatzverbrauch werden Kriterien wie Genauigkeit und Güte der gewonnen Information, aber auch die Anwendbarkeit auf bestimmte Formen sozialer Netzwerke (assoziativ und dissassoziativ) eingesetzt. Die Bewertungen werden beispielsweise durch Spinnendiagramme visualisiert. Das Proseminar bietet neben der üblichen Einführung in das wissenschaftliche Arbeiten spannende neue Formen des kollaborativen Forschen und Publizieren auf dem Web geübt. So werden die fachlichen Themen in Zweiergruppen mittels einer Wiki-Buch Plattform erarbeitet. Zusätzlich werden Gruppen zum fachlichen Begutachten, zum Web-Design und zur Animation von Algorithmen gebildet. Die Ergebnisse des Proseminars werden daher nicht in einer Schublade verschwinden, sondern auf dem Web als Open Content publiziert.
Knowledge Graphs Praktikum
Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.).
Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.
Data Visualisation and Analytics
This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.