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OFFERED COURSES

Term Type Title Workload
WS20 Lecture Applied Natural Language Processing and Text Mining 4 SWS, 6 ECTS
Lecture Key Competences in Computer Science 4 SWS, 9 ECTS
Seminar Selected Topics in Data & Knowledge Engineering 2 SWS, 3 ECTS
Earlier SS20 WS19 SS19 WS18

Assignment of Courses to Degree Programs

The table shows the degree programs and course areas for which the credits from our courses can be used.
(Click on the table to enlarge it or click here to download the table as a PDF.) 

[WS20] Applied Natural Language Processing and Text Mining

By completing 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

More Info (coming soon)   Moodle Course

Day Time Periodicity Duration Room Type
Thu. 10:15 - 11:45 weekly 29.10.2020 - 11.02.2021 FC E.10 (00.10) lecture
Thu. 12:15 - 13:45 weekly 05.11.2020 - 11.02.2021 FC E.10 (00.10) exercise

[WS20] 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

More Info (coming soon)     Moodle Course

Day Time Periodicity Duration Room Type
Wed. 10:15 - 11:45 weekly 28.10.2020 - 10.02.2021 FC E.10 (00.10) lecture
Wed. 12:15 - 13:45 weekly 04.11.2020 - 10.02.2021 FC E.10 (00.10) tutorial

[WS20] Selected Topics in Data & Knowledge Engineering

Seminar participants will explore current research trends and established approaches in the field of Data and Knowledge Engineering. 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

More Info (coming soon)     Moodle Course

Day Time Periodicity Duration Room Type
Wed. 14:15 - 15:45 weekly 28.10.2020 - 10.02.2021 FC E.10 (00.10) seminar
zuletzt bearbeitet am: 08.09.2020