• Introduction to the concepts and directions of traditional linguistics
• Classical tasks of computational linguistics
• Presentation of different methods for language processing from tokenization to sentiment analysis
• Different NLP systems and computational linguistic analysis models
• Discussion of the current state of research and further research ideas
• Practical introduction to basic methods of automatic language processing

• Students are familiar with different tasks and methods of computational linguistics and are able to implement some selected ones practically.

Lecture, practical exercises, presentations, discussions, feedback.

Continuous assessment: Written final examination, ongoing delivery of implementations, presentations.

• ML definition, application areas and classification of ML algorithms (Supervised, Unsupervised, Reinforcement Learning)
• Classical ML algorithms: kNN, Decision Trees, Naïve Bayes, NN, SVM, Ensemble Learning and Random Forest
• Typical approach to ML projects: Define requirements, collect, filter and represent data, define and extract features, deploy algorithms and evaluate their performance, improve iterative ML pipeline.
• Introduction to Deep Learning: CNN, RNN, Generative Networks

• Students are able to independently carry out ML projects and to correctly apply and critically evaluate different ML algorithms and methods.

Theory transfer in class, discussion of practical examples, own ML-project

Continuous assessment: Participation in discussions, elaboration of exercise examples, own ML-project, written exam

• Probability
• Analyzing, filtering and visualizing data
• Testing hypotheses
• Statistical estimators
• Experiment Design
• Approach to statistical projects

• Students are able to independently carry out statistical projects and to correctly apply and critically evaluate various statistical methods. The students have acquired the basics to understand more advanced courses such as ML.

Theory transfer in class, discussion of practical examples, own project

Continuous assessment: Activity in lectures and exercises: Participation in discussions, elaboration of exercise examples, own statistical project, written exam.

• Different types of language resources (terminology, lexicon, controlled vocabulary, thesaurus etc.).
• Methods for representing, creating, disseminating and using multilingual language resources, including the Linguistic Linked Open Data (LLOD) approach and linguistic Data Science in general.
• Multilingual and cross-lingual methods for improving communication using language resources and computational linguistic approaches.
• Practical examples from the field of LLOD

• Students are familiar with different types of language resources and know methods for their creation, dissemination, representation and use. Students are particularly familiar with the concept and possible applications of Linguistic Linked Open Data (LLOD). Students are familiar with the publication and use of language resources in LLOD.

Lecture/lecture, discussion, case solutions.

Final exam: Final Written Exam.

• Introduction to different types of translation technologies from computer assisted translation (CAT) to automated machine translation.
• Critical analysis of the perception of different technologies in the company and advantages and disadvantages of each technology
• Overview of different tools and available systems in each technology
• Overview of the current state of research and interesting open research questions in this larger topic area
• Insights into methods of quality improvement from pre- and post-editing to the revision process in translation
• Practical work with a system of computer-aided translation

• Students will be able to distinguish between different types of translation technology and will be aware of the areas of application of the various technologies, having learned in depth about their differences, both theoretically and practically.

Lecture/lecture, practical exercises, discussions, feedback, case solutions.

Continuous assessment: Written final exam, practical exercises, presentations.

The course provides programming and algorithmic skills using the Python programming language. Basic knowledge is assumed. Central foundations are reviewed at the beginning of the course.

Selected techniques such as debugging, code analysis, and the use of Git for version control are also discussed.

In addition, the following topics are covered:

• programming paradigms and basic concepts of Python
• objects, data types, variables, functions, and data structures
• control structures, error handling, and object-oriented programming
• regular expressions, search and sorting algorithms
• encoding, data formats CSV, XML, and JSON
• use of Application Programming Interfaces
• fundamentals of Information Retrieval

• Upon completion of the course, students implement and test modular Python solutions for clearly defined problems, selecting appropriate data structures and algorithms. They analyze and improve existing Python programs, systematically apply debugging and basic code analysis, and use Git. They model and implement object-oriented structures in Python and handle errors robustly. They process and integrate structured data (CSV, XML, and JSON) as well as API endpoints and resilient input handling. They also develop and apply search patterns using regular expressions.

Lecture, code enactment, discussion of program examples, group work, and student presentations on selected topics

Continuous assessment: Partial assessments in the form of in-class tests, group projects, and presentations.

This course teaches basic concepts of object-oriented programming using the Python programming language. Concepts of programming languages such as control structures, elementary data types, data structures, classes, objects and functions are taught. Furthermore, the design of programs, their analysis and techniques for debugging, tracing and testing are taught.

The course covers the following topics in particular:

• Basics of programming
• Variables and data types
• Operators
• Control structures
• Error handling
• Basics of object orientation
• Sorting algorithms
• Search algorithms

• Upon completion of the course, students will be able to design and iteratively implement a modular Python program using appropriate data structures and control mechanisms. They will systematically debug errors and refactor and improve existing code while ensuring readability and modularity. They conduct code reviews in small groups and justify their feedback. They present and explain proposed solutions as well as the design decisions they have made.

Individual implementation tasks, code reviews, small-group work, and presentations of the results achieved

Continuous assessment: Partial assessments in the form of individual implementation tasks, code reviews, group projects, and presentations.

• Critical analysis of classical ML algorithms.
• Standard DL algorithms: CNN, RNN, Generative Networks
• Modern DL architectures for Natural Language Processing (NLP): Attention, Transformer, GPT, BERT etc.
• Applications of ML in general and DL in particular to NLP: text understanding, translation, speech and text generation, web search, knowledge generation
• Limitations of DL

• Students will be able to independently carry out DL projects, apply them to NLP and correctly use and critically evaluate various algorithms and methods.

Theoretical lessons, discussion of practical examples, own DL-project

Module exam

• Critical analysis of classical ML algorithms.
• Standard DL algorithms: CNN, RNN, Generative Networks
• Modern DL architectures for Natural Language Processing (NLP): Attention, Transformer, GPT, BERT etc.
• Applications of ML in general and DL in particular to NLP: text understanding, translation, speech and text generation, web search, knowledge generation
• Limitations of DL

• Students will be able to independently carry out DL-projects, apply them to NLP and correctly use and critically evaluate various algorithms and methods.

Theoretical lessons, discussion of practical examples, own DL-project

Module exam

• Basics of information design
• Target group oriented design of media and information
• Design development on the basis of cognitive science principles
• Basics of Gestalt and perception psychology
• Methods of information design for different media
• Applications in web, virtual and augmented reality etc.

• Students will be able to apply the principles of information design media so that the intended audience can use the information most efficiently and effectively.

Teaching theory in class, interdisciplinary lecture series, discussion of practical examples; own information design project.

Continuous assessment: Partial performance through active participation in discussions and the elaboration of exercise examples, own information design project, written examination.

• Retrieval models: boolean, vector space, probabilistic.
• Representation of content: Free text search, documentation languages, special logics, indexing, etc.).
• Machine-Learning-concepts and techniques: clustering, classification
• Deep Learning in Information Retrieval
• Web Retrieval: Link Analysis, Crawling, Search Engines

• Students will be familiar with the basic aspects and methods of Information Retrieval (IR) and will be able to independently carry out IR projects and to correctly apply and critically evaluate different IR methods.

Theory transfer in class, discussion of practical examples; own IR project

Continuous assessment: Partial performance through active participation in discussions and the elaboration of exercise examples, own IR project, written examination.

• Speech Technologies and Automatic Speech Recognition (ASR)
• Fundamentals of Phonetics and Phonology
• Neural Networks for Speech Technologies
• Introduction to dialogue systems
• Practical introduction to ASR and speech-to-speech systems

• Students have acquired a basic overview of various aspects, algorithms and methods of Speech Technologies and can implement selected methods themselves.

Lecture, practical exercises, presentations, discussions, feedback.

Final exam: Written final examination, partial performance in the form of practical exercises.

• Introduction to the different approaches of machine translation from statistical to rule-based to neural and hybrid approaches.
• Introduction to basic concepts and algorithms of statistical machine translation
• Introduction to basic concepts and algorithms of neural machine translation
• Critical analysis of the advantages and disadvantages of individual systems as well as the goal and purpose of the respective approaches
• Basic knowledge of machine translation evaluation methods
• Practical introduction to concrete translation models

• Students have received a sound overview of the areas of machine translation, know the areas of application as well as advantages and disadvantages. Furthermore, students are able to implement and evaluate one of these models themselves, since they have also acquired basic knowledge of evaluation methods.

Lecture/lecture, practical exercises, work assignments, discussions, feedback, case solutions.

Continuous assessment: Written final exam, practical exercises, presentations.

• Transcultural communication from the perspective of different disciplines (with a focus on translation studies)
• communication science basics intra-, inter- and multilingual barriers and transculturality
• online collaborative translation as transcultural communication
• conceptual issues and problems
• technology assessment and ethical considerations
• transcultural communication and translation in teams

• Students have developed an awareness and sensitivity for transcultural communication and refresh basic concepts of communication science. Students have acquired theoretical skills for practical collaboration in transcultural teams and the latest scientific knowledge on the field of ethical issues around a technology assessment.

Lecture/lecture, discussion, case studies.

Final exam: Final Written Exam.

• Psychological aspects of HCI
• Usability
• User research
• Benchmarking usability
• Interaction design
• Prototyping
• Usability research and testing methods
• Usability in practice

• Upon completion of the course, students will have a solid understanding of human-computer communication, the underlying psychological and technical aspects, and innovations in human-computer interaction technologies.

Case studies, practical exercises, lecture

Continuous assessment: Case studies, group exercise, written final exam.

Organizational possibilities for structuring software development in the form of process models, such as the waterfall model, spiral model and agile models, are presented. The technical aspects of software engineering focus on the creation of object-oriented systems and their modelling in the field of machine learning.

The course covers the following topics in particular:

• requirements engineering
• use cases
• High Level Design
• Software engineering aspects in the area of machine learning
• Selected UML diagrams
• Process models

• After completing the course, students will be able to understand and apply software engineering processes for machine learning projects.

Blended learning, guest lectures, experiential learning, coaching

Continuous assessment: Group work, written final exam.

• Theoretical elaboration of different architectures in the field of neural machine translation.
• Theoretical overview of the current state of research and interesting current research topics, e.g. machine translation with only little available training data
• Critical discussion of advantages and disadvantages of the respective systems
• Analysis and discussion of current practice regarding the application of machine translation systems in companies
• Practical development of concrete current models of neural machine translation as well as their evaluation methods

• Students have a sound knowledge of various models and architectures in neural machine translation, know their areas of application as well as advantages and disadvantages. The previously acquired knowledge of evaluation is further deepened in this course. Students are also able to train and evaluate a neural machine translation model themselves.

Lecture/lecture, practical exercises, work assignments, discussions, feedback, case solutions.

Continuous assessment: Written final exam, practical exercises, presentations.

• Systematic research and reception of scientific works
• Correct citation
• Language register: formal vs. informal
• academic terminology and phrasing
• Structure of a paragraph
• Structure of a scientific paper
• Linguistic presentation of the chosen method, achieved results and resulting discussion
• describing statistical and qualitative data

• Students are able to systematically conduct scientific research and analyse scientific work. They master the basics of scientific citation and are able to structure their own scientific work sensibly. Furthermore, they are familiar with the usual language registers and formulations in the scientific context and are able to apply them.

Writing and research exercises, correction tasks, feedback, discussion, problem-based learning.

Final exam: Writing and research exercises, written seminar paper.

• Introduction to the Austrian and European legal system
• Introduction to data protection law
• Protection of privacy and general protection of personality
• Principles of processing personal data
• Roles under data protection law
• Data subject rights and obligations of the processor
• Insight into data security concepts
• Privacy by Design and Privacy by Default
• E-Privacy
• Basics of cyber security
• Tasks and powers of the data protection supervisory authority and procedural aspects

• Students will be able to recognise and classify common legal issues and to put into practice what they have learned in theory (e.g. exercising the rights of data subjects).

• Theory transfer in lectures
• Discussion of practical examples

Continuous assessment: - Activities during lectures and exercises: Participation in discussions

- Participation

- Written examination

Project management is the application of knowledge, skills, tools and techniques to project activities in order to meet project requirements. The project manager is responsible for meeting the expectations of the stakeholders in the project.

The course covers in particular the following contents:

• The immersion in the knowledge areas of project management (for example: Integration management, time management, cost management, quality management and risk management).
• Project management across cultural boundaries
• The management of virtual teams
• Legal aspects in IT projects

• After completion of the course, the students are able to create a project management plan for a given requirements definition and to adapt it during the entire project life cycle, taking into account legal IT aspects.

Case studies, lecture

Continuous assessment: Written final examination, preparation of a case study.

• Independent work on a subject relevant topic based on the technical topics of the compulsory elective modules in the third semester at an academic level under the supervision of a supervisor
• Elaboration of the master thesis

• After completion of the course, students will be able to identify a relevant problem and analyse it in a scientifically sound manner.

• After completion of the course, the students are able to correctly apply scientific methods linked to specialist knowledge.

• After completion of the course, the students are able to comprehensively work out a topic and to write it as a master's thesis.

Independent work supported by coaching

Final exam: Seminar paper

• Refresher course on research methodology
• Refresher and consolidation of good practice in scientific work
• Presentation techniques and types for scientific work
• Methods for the preparation of a master thesis concept

• Students have a good overview of the procedure for developing a current state of research in particular for their research topic as well as possibilities of research design. They develop the research question and tentative method for their chosen research topic and have gained a good overview of the larger scientific topic area. Furthermore, students know how to present scientific content.

Group work, discussion, presentation, feedback, interactive lecture with practical exercises.

Continuous assessment: Oral presentation, written work in the form of an exposé.