[WS19] Applied Natural Language Processing and Text Mining

By completing the the course, participants will obtain the knowledge and skills to solve a wide range of applied problems in Natural Language Processing. To achieve this goal, the participants will get to know successful methods for solving sub-problems, such as text representation, information extraction, text mining, language modeling, and similarity detection. The participants will understand the conceptual requirements of specific NLP tasks and be able to devise approaches to address these tasks in practice. The participants will be able to assess the strengths and limitations of state-of-the-art NLP approaches and to propose solutions for interdisciplinary NLP problems.

Workload: 4 SWS (2 Lecture / 2 Exercise), 6 ECTS

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[WS19] Information Visualization

The course participants will acquire the knowledge and skills necessary to visualize a wide range of data for analysis, exploration, and information purposes. The participants will learn the fundamentals of human perception, design and interaction principles as well as elemental visualization techniques necessary to create visualizations suitable for the given type of data and the intended use case. The participants will also know the requirements that different data types and levels of complexity impose on the visualization as well as how to evaluate the quality of information visualizations. Much of the data covered in the course is abstract, i.e., the data has no spatial reference and thus cannot be mapped trivially to geometric visuals. Examples of abstract data include survey results, database contents, or genome information. The participants will be challenged with data from many more applications in industry, business, science and everyday life.

Workload: 4 SWS (2 Lecture / 2 Exercise), 6 ECTS

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[WS19] Selected Topics in Data & Knowledge Engineering

Seminar participants will explore current research trends and established approaches in the field of Data Science field. The participants can choose to complete either the theoretical or the practical research project track as part of this seminar.

Workload: 2 SWS, 3 ECTS

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[SS20] Key Competences in Computer Science

Course participants will receive a comprehensive introduction into the essentials of conducting research in computer science and into state-of-the-art development frameworks (e.g. backend, frontend, continuous integration, deployment) and tools.  

The course consists of two major parts:

Part A covers the fundamentals of conducting research in computer science, such as academic literature research and literature management, academic writing, designing and performing experiments, handling data and ensuring reproducibility.

Part B introduces state-of-the-art development frameworks and tools for, e.g., scripting (Python, Shell), Web technologies (HTML, JavaScript, D3), testing and continuous integration (unit tests, Travis). 

Through practical work on projects, students will get deeper into selected topics and technologies and acquire practical skills necessary to solve various real-world problems in computer science.

Workload: 4 SWS (2 Lecture / 2 Exercise), 9 ECTS

[SS20] Information Retrieval

By completing the course, students will get to know the importangt information retrieval tasks, e.g. ,Web search and recommendation. The participants will understand the conceptual requirements of specific retrieval tasks and be able to devise retrieval approaches consisting of suitable data structures and algorithms to address these tasks. The participants will be able to evaluate the strengths and weaknesses of retrieval approaches and to implement suitable retrieval approaches to solve complex practical information retrieval problems.

Workload: 4 SWS (2 Lecture / 2 Exercise), 6 ECTS

[SS20] Selected Topics in Data Science

Seminar participants will explore current research trends and established approaches in the Data Science and the Information Science fields. Participants can choose to complete either a theoretical or a practical research project.

Workload: 2 SWS, 3 ECTS