Multilingual Technologies*

Multilingual Technologies combines language and IT. This Master's degree program, which is unique in Austria, is offered in cooperation with the Center for Translation Studies at the University of Vienna and is aimed at all those who already have a Bachelor's degree in engineering or translation science and are interested in language technologies as well as multilingual solutions and concepts. The interdisciplinary character of the degree program qualifies students for future-oriented professional fields, for example in language technology or in the field of machine translation.

Department

Engineering

Topic

Technologies

Highlights

Interdisciplinary character
Future-oriented professional field
Master's degree program unique in Austria

Facts

Final degree: Master of Science (MSc)

Duration of course: 4 Semesters

Organisational form: part-time

Tuition fee pro semester € 363,36¹ + ÖH premium + contribution²

ECTS: 120 ECTS

Language of instruction: English

Currently no application possible

Study places: 30

¹ Tuition fees for students from third countries € 727,- per semester. Details on tuition fees can be found in the general fee regulations.
² for additional study expenses (currently up to € 83,- depending on degree program and year)

Before the studies

You already have a Bachelor’s degree in engineering or in translation studies and are interested in language technologies as well as multilingual solutions and concepts. With this joint Master’s degree of FH Campus Wien and the Centre for Translation Studies at the University of Vienna, you can combine your knowledge of language and IT in a future-oriented education and training profile.

Why you should study with us

Participate in interdisciplinary student or research projects

This way, fun and experience are guaranteed!

Practical training on campus

Modern laboratory equipment and high-tech research facilities enable practice-oriented teaching.

Unique job opportunities

Obtain additional certificates while still studying and increase your market value.

Relevant admission requirement

The relevant admission requirement is

a completed relevant Bachelor's degree (e.g. Computer Science and Digital Communications at FH Campus Wien or Transcultural Communication at the University of Vienna) or
the completion of an equivalent degree at a recognized domestic or foreign post-secondary educational institution.

The following specialist knowledge is also required:

Basic knowledge of language technologies and specialist communication
- fulfillment by completing the Bachelor's degree program in Transcultural Communication or
- completion of the extension curriculum language technologies and specialist communication at the Center for Translation Studies
Fundamentals of computer science, basic methods and tools of software engineering
- fulfillment by completing the Bachelor's degree program in Computer Science and Digital Communications or
- completion of the computer science extension curriculum (for students of the University of Vienna) at FH Campus Wien

Language requirements for admission

The program is taught entirely in English. The required language level according to the Common European Framework of Reference for Languages (CEFR) is at least

English - level B2

Legalization of foreign documents

Applicants may require legalization of documents from countries other than Austria in order for them to have the evidential value of domestic public documents. Information on the required legalizations can be found here in PDF format.

Legalization of foreign documents in higher education
Federal ministry Education, Science and Research

open

Translation of your documents

For documents that are neither in German nor English, a translation by a sworn and court-certified interpreter is required. Your original documents should have all the necessary legalization stamps before translation so that the stamps are also translated. The translation must be firmly attached to the original document or a legalized copy.

Online application - uploading documents

As part of your online application, upload scans of your original documents including all required legalization stamps. For documents not issued in German or English, scans of the corresponding translations must also be uploaded. The head of the study program decides on the equivalence of international (higher) education qualifications. Therefore, your documents can only be checked as part of the ongoing application process.

Your path to studying at FH Campus Wien begins with your registration on our application platform. In your online account, you can start your application directly or activate a reminder if the application phase has not yet started.

Documents for your online application

Proof of identity
- passport or
- identity card or
- Austrian driving license (proof of citizenship required) or
- residence permit (proof of citizenship required)
Proof of change of name, if applicable (e.g. marriage certificate)
Proof of fulfillment of the relevant admission requirement
- degree certificate and
- Transcript of Records or Diploma Supplement
- If you have not yet completed your studies, please upload proof of all courses completed to date as part of the relevant degree program, including ECTS credits.
Proof of your extension curriculum
Proof of language skills
- English level B2 of the Common European Framework of Reference for Languages (CEFR). Please inform yourself here on the website of the University of Vienna about the recognized English certificates at level B2 and upload the corresponding certificate.
- If English is your first language, proof of another language is also required.
Curriculum vitae in tabular form in English
Letter of motivation in English
Proof of additional qualifications or specific additional training, if applicable
Legalizations and translations, if applicable (details in the tab "Foreign documents and degrees")

Your application is valid once you have completely uploaded the required documents. If you do not have all the documents at the time of your online application, please submit them to the secretary's office by email as soon as you receive them.

After completing your online application, you will receive an email confirmation with information on the next steps.

Admission procedure: the admission procedure includes an interview with members of the admission committee (representatives of the University of Vienna and FH Campus Wien). This interview will take place online until further notice. You will receive the date for the admission procedure from the secretary's office.

Objective
It is our objective to offer a study place to those persons who complete the admission procedure with the best results. The test procedures are oriented towards the skills required for the intended profession.
Procedure
In the interview, you will answer some basic subject-specific questions, some questions about yourself, and explain your motivation for choosing the program (duration: approx. 30 minutes). If you have not yet reached the required entry level for the degree program, you will receive recommendations after admission on how best to prepare yourself in a subject-specific way.
Criteria
The admission criteria are based on performance only. You will receive points for the oral interview, which will determine the ranking of the candidates. Geographical assignments of the applicants have no influence on the admission. The admission requirements must be met. The entire process as well as all test results and evaluations of the admission procedure are documented and archived in a comprehensible manner.

During the studies

This Master's degree program, which is unique in Austria and also innovative by international standards, focuses on language technologies, methods for their generation and use, and on language resources. It has a strong interdisciplinary character due to the combination of translational, transcultural, computer science and linguistic disciplines.

The joint Master's degree program will be based in the Department Engineering of FH Campus Wien and the Center for Translation Studies of the University of Vienna. It thus combines the special profile elements, professional strengths and scientific expertise of both institutions to create a future-oriented interdisciplinary education and training profile.

Along with qualifications for basic research, you will acquire skills in applied research. Students gain knowledge of basic concepts of language technologies and language resources with a special focus on multilingual solutions and concepts, as well as comprehensive methodological knowledge and practical skills in current research techniques. In addition, students acquire specialized expertise in one area of language technologies, e.g. translation technologies or multilingual information extraction.

Curriculum

Module Language Technologies

3 SWS

6 ECTS

Introduction to Computational Linguistics | ILV
Introduction to Computational Linguistics | ILV
3 SWS 6 ECTS
Content
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
Teaching method
Lecture, practical exercises, presentations, discussions, feedback.
Examination
Continuous assessment: Written final examination, ongoing delivery of implementations, presentations.
Literature
Clark, A., Fox, C., & Lappin, S. (Eds.). (2013). The handbook of computational linguistics and natural language processing. John Wiley & Sons.
Eisenstein, J. (2018). Natural language processing. github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf [Stand: 30.3.2020]
Jurafsky, D., & Martin, J. H. (2019). Speech and Language Processing (3rd (draft) ed.) web.stanford.edu/~jurafsky/slp3/ [Stand: 30.03.2020].
Mitkov, R. (Ed.). (2004). The Oxford handbook of computational linguistics. Oxford University Press.
Teaching language
Englisch

3 SWS

6 ECTS

Module Machine Learning Fundamentals for Language Processing

5 SWS

10 ECTS

Introduction to Machine Learning for Language Processing | ILV
Introduction to Machine Learning for Language Processing | ILV
3 SWS 6 ECTS
Content
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
Teaching method
Theory transfer in class, discussion of practical examples, own ML-project
Examination
Continuous assessment: Participation in discussions, elaboration of exercise examples, own ML-project, written exam
Literature
Bishop, C. M. (2007). Pattern Recognition and Machine Learning. Springer.
Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media.
Mitchell, T. (1997). Machine Learning. McGraw-Hill.
Theodoridis, S. (2008). Pattern Recognition. Elsevier.
Eisenstein, J., (2019). Introduction to Natural Language Processing. The MIT Press.
Teaching language
Englisch

3 SWS

6 ECTS

Statistical Methods for Language Processing | ILV
Statistical Methods for Language Processing | ILV
2 SWS 4 ECTS
Content
Probability
Analyzing, filtering and visualizing data
Testing hypotheses
Statistical estimators
Experiment Design
Approach to statistical projects
Teaching method
Theory transfer in class, discussion of practical examples, own project
Examination
Continuous assessment: Activity in lectures and exercises: Participation in discussions, elaboration of exercise examples, own statistical project, written exam.
Literature
Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. MIT Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
Hogg, R. V., Tanis, E. A., & Zimmerman, D. L. (2010). Probability and statistical inference. Pearson/Prentice Hall.
Teaching language
Englisch

2 SWS

4 ECTS

Module Multilingual Communication

4 SWS

8 ECTS

Multilingual and Crosslingual Methods and Language Resources | VO
Multilingual and Crosslingual Methods and Language Resources | VO
2 SWS 4 ECTS
Content
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
Teaching method
Lecture/lecture, discussion, case solutions.
Examination
Final exam: Final Written Exam.
Literature
Ammar, W., Mulcaire, G., Tsvetkov, Y., Lample, G., Dyer, C., & Smith, N. A. (2016). Massively multilingual word embeddings. arXiv preprint arXiv:1602.01925.
Bosque-Gil, J., Gracia, J., Montiel-Ponsoda, E., & Gómez-Pérez, A. (2018). Models to represent linguistic linked data. Natural Language Engineering, 24(6), 811-859.
Chiarcos, C., McCrae, J., Cimiano, P., & Fellbaum, C. (2013). Towards open data for linguistics: Linguistic linked data. In New Trends of Research in Ontologies and Lexical Resources (pp. 7-25). Springer, Berlin, Heidelberg.
Cimiano, P., Chiarcos, C., McCrae, J. P., & Gracia, J. (2020). Linguistic Linked Data in Digital Humanities. In Linguistic Linked Data (pp. 229-262). Springer, Cham.
Forkel, R. (2014). The cross-linguistic linked data project. In 3rd Workshop on Linked Data in Linguistics: Multilingual Knowledge Resources and Natural Language Processing (p. 61).
McCrae, J. P., Moran, S., Hellmann, S., & Brümmer, M. (2015). Multilingual linked data. Semantic Web, 6(4), 315-317.
McCrae, J. P., Bosque-Gil, J., Gracia, J., Buitelaar, P., & Cimiano, P. (2017). The Ontolex-Lemon model: development and applications. In Proceedings of eLex 2017 conference (pp. 19-21).
Ruder, S., Vulić, I., & Søgaard, A. (2019). A survey of cross-lingual word embedding models. Journal of Artificial Intelligence Research, 65, 569-631.
Teaching language
Englisch

2 SWS

4 ECTS

Translation Technologies | VO
Translation Technologies | VO
2 SWS 4 ECTS
Content
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
Teaching method
Lecture/lecture, practical exercises, discussions, feedback, case solutions.
Examination
Continuous assessment: Written final exam, practical exercises, presentations.
Literature
Baker, M., & Saldanha, G. (2019). Routledge encyclopedia of translation studies. Routledge.
Bowker, L. (2014). Computer-aided translation: translator training. In Routledge encyclopedia of translation technology (pp. 126-142). Routledge.
Gambier, Y., & Van Doorslaer, L. (Eds.). (2010). Handbook of translation studies (Vol. 1). John Benjamins Publishing.
Jakobsen, A. L., & Mesa-Lao, B. (Eds.). (2017). Translation in transition: between cognition, computing and technology (Vol. 133). John Benjamins Publishing Company.
Teaching language
Englisch

2 SWS

4 ECTS

Module Software Development for Language Technologies

3 SWS

6 ECTS

Programming and Algorithms for Language Technologies | VO
Programming and Algorithms for Language Technologies | VO
1 SWS 2 ECTS
Content
This course teaches programming concepts using the Python programming language. Knowledge of basic concepts and elemental programming experience are prerequisites. Fundamentals are repeated at the beginning of the course.
Techniques like Debugging and Tools like Git for Version control are discussed.
In addition, the following topics are discussed:
* Data structures
* Regular expressoins and search algorithms (A* algorithm, Beam search, ...)
* Usage of Application Programming Interfaces (APIs), JSON, XML
* Basics of Information Retrieval
Teaching method
Lecture/Talk.
Examination
Continuous assessment: Partial performances in the form of individual work, group work and presentations.
Oral Final Exam.
Literature
Brooks, A. T. (2019). Python for Beginners: A Smarter Way to Learn Python in 5 Days and Remember it Longer. With Easy Step by Step Guidance and Hands on Examples. Arthur T. Books.
Chan, J. (2017). Learn Python in One Day and Learn It Well. Python for Beginners with Hands-on Project (Learn Coding Fast with Hands-On Project Book 1). CreateSpace Independent Publishing.
Lacey, N. (2019). Python by Example: Learning to Program in 150 Challenges. Cambridge University Press.
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
Teaching language
Englisch

1 SWS

2 ECTS

Programming and Algorithms for Language Technologies | UE
Programming and Algorithms for Language Technologies | UE
2 SWS 4 ECTS
Content
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
Teaching method
Small group work, practical exercises, presentation of results.
Examination
Continuous assessment: Partial performances in the form of group work and presentations.
Literature
Brooks, A. T. (2019). Python for Beginners: A Smarter Way to Learn Python in 5 Days and Remember it Longer. With Easy Step by Step Guidance and Hands on Examples. Arthur T. Books.
Chan, J. (2017). Learn Python in One Day and Learn It Well. Python for Beginners with Hands-on Project (Learn Coding Fast with Hands-On Project Book 1). CreateSpace Independent Publishing.
Lacey, N. (2019). Python by Example: Learning to Program in 150 Challenges. Cambridge University Press.
Teaching language
Englisch

2 SWS

4 ECTS

Module Applied Machine Learning for Language Processing
Applied Machine Learning for Language Processing
3 SWS 6 ECTS
Examination
: Participation in discussions, elaboration of exercise examples, own DL project, written exam

3 SWS

6 ECTS

Machine Learning Methods for Language Processing | VO
Machine Learning Methods for Language Processing | VO
1 SWS 2 ECTS
Content
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
Teaching method
Theoretical lessons, discussion of practical examples, own DL-project
Examination
Module exam
Literature
Chollet, F. (2018). Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITP-Verlags GmbH & Co. KG.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Teaching language
Englisch

1 SWS

2 ECTS

Machine Learning Methods for Language Processing | UE
Machine Learning Methods for Language Processing | UE
2 SWS 4 ECTS
Content
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
Teaching method
Theoretical lessons, discussion of practical examples, own DL-project
Examination
Module exam
Literature
Chollet, F. (2018). Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITP-Verlags GmbH & Co. KG.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Teaching language
Englisch

2 SWS

4 ECTS

Module Information Management for Language Data

5 SWS

10 ECTS

Information Design for Language Data | ILV
Information Design for Language Data | ILV
2 SWS 4 ECTS
Content
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.
Teaching method
Teaching theory in class, interdisciplinary lecture series, discussion of practical examples; own information design project.
Examination
Continuous assessment: Partial performance through active participation in discussions and the elaboration of exercise examples, own information design project, written examination.
Literature
Coates, K. & Ellison, A. (2014). Introduction to Information Design. Laurence King Publishing.
Katz, J. (2012). Designing Information: Human Factors and Common Sense in Information Design. John Wiley & Sons.
Weber, W. (2007). Kompendium Informationsdesign, Springer: Heidelberg, Berlin.
Pontis S., Babwahsingh M. (2023), Information Design Unbound, Bloomsbury Publishing.
Teaching language
Englisch

2 SWS

4 ECTS

Information Extraction and Retrieval for Multilingual Natural Language Data | ILV
Information Extraction and Retrieval for Multilingual Natural Language Data | ILV
3 SWS 6 ECTS
Content
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
Teaching method
Theory transfer in class, discussion of practical examples; own IR project
Examination
Continuous assessment: Partial performance through active participation in discussions and the elaboration of exercise examples, own IR project, written examination.
Literature
Baeza-Yates, R. & Ribeiro-Neto, B. (2011). Modern information retrieval: The concepts and technology behind search. Addison Wesley.
Croft, B., Metzler, D. & Strohman, T. (2009). Search Engines: Information Retrieval in Practice. Addison-Wesley.
Manning, C.D., Raghavan, P. & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
Teaching language
Englisch

3 SWS

6 ECTS

Module Language Technologies

3 SWS

6 ECTS

Speech Technologies | ILV
Speech Technologies | ILV
3 SWS 6 ECTS
Content
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
Teaching method
Lecture, practical exercises, presentations, discussions, feedback.
Examination
Final exam: Written final examination, partial performance in the form of practical exercises.
Literature
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Duda, R. O., Hart, P. E., & Stork, D. G. (1973). Pattern classification and scene analysis (Vol. 3). New York: Wiley.
Huang, X., Acero, A., Hon, H. W., & Foreword By-Reddy, R. (2001). Spoken language processing: A guide to theory, algorithm, and system development. Prentice hall PTR.
Levinson, S. E. (2005). Mathematical models for speech technology. John Wiley.
Vetterli, M., Kovačević, J., & Goyal, V. K. (2014). Foundations of signal processing. Cambridge University Press.
Teaching language
Englisch

3 SWS

6 ECTS

Module Machine Translation

3 SWS

5 ECTS

Basics in Machine Translation | ILV
Basics in Machine Translation | ILV
3 SWS 5 ECTS
Content
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
Teaching method
Lecture/lecture, practical exercises, work assignments, discussions, feedback, case solutions.
Examination
Continuous assessment: Written final exam, practical exercises, presentations.
Literature
Bahdanau, D., Cho, K., and Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. InInternational Conference on Learning Representations
Goldberg, Y. (2016). A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research, 57, 345-420.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Kenny, D. (2018). Machine translation. In The Routledge Handbook of Translation and Philosophy (pp. 428-445). Routledge.
Koehn, P. (2009). Statistical machine translation. Cambridge University Press.
Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002, July). BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 311-318). Association for Computational Linguistics.
Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, pp. 3104–3112.
Teaching language
Englisch

3 SWS

5 ECTS

Module Multilingual Communication

2 SWS

3 ECTS

Transcultural Communication | VO
Transcultural Communication | VO
2 SWS 3 ECTS
Content
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
Teaching method
Lecture/lecture, discussion, case studies.
Examination
Final exam: Final Written Exam.
Literature
Ishida, Toru (ed), Culture and Computing. Computing and Communication for Crosscultural Interaction, Berlin / Heidelberg: Springer-Verlag, 2010.
Jiménez-Crespo, M. A. (2017). Crowdsourcing and online collaborative translations: Expanding the limits of translation studies (Vol. 131). John Benjamins Publishing Company.
Milhouse, V. H., Asante, M. K., & Nwosu, P. O. (2001). Transcultural realities. Sage.
Welsch, W. (1999). Transculturality: The puzzling form of cultures today. Spaces of culture: City, nation, world, 13(7), 194-213.
Teaching language
Englisch

2 SWS

3 ECTS

Module Applied Software Engineering for Computational Linguists

5 SWS

10 ECTS

Human-Computer Interaction for Computational Linguists | ILV
Human-Computer Interaction for Computational Linguists | ILV
2 SWS 4 ECTS
Content
Psychological aspects of HCI
Usability
User research
Benchmarking usability
Interaction design
Prototyping
Usability research and testing methods
Usability in practice
Teaching method
Case studies, practical exercises, lecture
Examination
Continuous assessment: Case studies, group exercise, written final exam.
Literature
Cooper, A. Reimann, R., Cronon, D. & Noessel, C. (2014). The Essentials of Interaction Design. Wiley, 4th Edition.
Shneiderman, B. (2016). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Global Edition.
Teaching language
Englisch

2 SWS

4 ECTS

Software Engineering for Language Technologies | ILV
Software Engineering for Language Technologies | ILV
3 SWS 6 ECTS
Content
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
Teaching method
Blended learning, guest lectures, experiential learning, coaching
Examination
Continuous assessment: Group work, written final exam.
Literature
Sommerville, I. (2015). Software Engineering. Pearson Education Limited, 10th Edition.
Stephens, R. (2015). Beginning Software Engineering. John Wiley & Sons, 1st Edition.
Teaching language
Englisch

3 SWS

6 ECTS

Module Machine Translation

3 SWS

5 ECTS

Advanced Machine Translation | ILV
Advanced Machine Translation | ILV
3 SWS 5 ECTS
Content
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
Teaching method
Lecture/lecture, practical exercises, work assignments, discussions, feedback, case solutions.
Examination
Continuous assessment: Written final exam, practical exercises, presentations.
Literature
Gehring, J., Auli, M., Grangier, D., Yarats, D., and Dauphin, Y. N. (2017). Convolutional sequence to sequence learning.arXivpreprint arXiv:1705.03122.
Minh-Thang Luong Neural Machine Translation Ph.D. Dissertation, 2016. github.com/lmthang/thesis/blob/master/thesis.pdf [Stand: 30.03.2020]
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., & Polosukhin, I. (2017). Attentionis all you need. In Advances in Neural Information Processing Systems, pp. 5998–6008.
Teaching language
Englisch

3 SWS

5 ECTS

Module Research Design and Academic Writing

2 SWS

5 ECTS

Academic Writing | ILV
Academic Writing | ILV
2 SWS 5 ECTS
Content
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
Teaching method
Writing and research exercises, correction tasks, feedback, discussion, problem-based learning.
Examination
Final exam: Writing and research exercises, written seminar paper.
Literature
Macgilchrist, Felicitas (2014) Academic writing. Vol. 4087, UTB.
Mautner, Gerlinde (2019) Wissenschaftliches Englisch: stilsicher schreiben in Studium und Wissenschaft. Vol. 3444. UTB.
Swales, J. M., & Feak, C. B. (2012). Academic writing for graduate students: Essential tasks and skills (Vol. 3). Ann Arbor: University of Michigan Press.
Voss, Rödiger (2018) Wissenschaftliches Arbeiten: ... leicht verständlich!. UTB.
Teaching language
Englisch

2 SWS

5 ECTS

(10 ECTS of your choice)

Module Internship FH Campus Wien

2 SWS

10 ECTS

Internship FH Campus Wien | PR
Internship FH Campus Wien | PR
2 SWS 10 ECTS
Content
Students complete an internship (PR), 10 ECTS, 2 SWS (examination-immanent) or alternatively a research project, 10 ECTS, 2 SWS (examination-immanent).
The internship requires approx. 225 hours, the internship report approx. 25 hours.
The choice of internship or research project must be approved in advance by the director of studies.
It is strongly recommended to complete a professional internship. If no internship place is available, a smaller research project as described above can be completed under the guidance of a supervisor.
Teaching method
Professional internship
Examination
Final exam: Submission of an internship report.
Literature
-
Teaching language
Englisch

2 SWS

10 ECTS

Module Internship Universität Wien

2 SWS

10 ECTS

Internship Universität Wien | PR
Internship Universität Wien | PR
2 SWS 10 ECTS
Content
Students complete an internship (PR), 10 ECTS, 2 SWS (examination-immanent) or alternatively a research project, 10 ECTS, 2 SWS (examination-immanent).
The internship requires approx. 225 hours, the internship report approx. 25 hours.
The choice of the internship or the research project must be approved in advance by the director of studies.
It is strongly recommended to complete a professional internship. If no internship place is available, a smaller research project as described above can be completed under the guidance of a supervisor.
Teaching method
Professional internship
Examination
Final exam: Submission of an internship report.
Literature
-
Teaching language
Englisch

2 SWS

10 ECTS

Module IT Management for Computational Linguists

2 SWS

4 ECTS

Data Protection and Privacy for Computational Linguists | ILV
Data Protection and Privacy for Computational Linguists | ILV
1 SWS 2 ECTS
Content
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
Teaching method
Theory transfer in lectures
Discussion of practical examples
Examination
Continuous assessment: - Activities during lectures and exercises: Participation in discussions
- Participation
- Written examination
Literature
Forgó (Hrsg.), Grundriss Datenschutzrecht (2018).
Teaching language
Englisch

1 SWS

2 ECTS

IT Project Management for Computational Linguists | ILV
IT Project Management for Computational Linguists | ILV
1 SWS 2 ECTS
Content
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
Teaching method
Case studies, lecture
Examination
Continuous assessment: Written final examination, preparation of a case study.
Literature
Forgó, N., Mosing, M. W. & Otto, G. (2012). Informationsrecht. Springer.
Kerzner, H. (2017). Project Management: A Systems Approach to Planning, Scheduling, and Controlling. Wiley, 12th edition.
Project Management Institute (2017). A Guide to the Project Management Body of Knowledge. Pmbok Guides, 6th edition.
Teaching language
Englisch

1 SWS

2 ECTS

Module Master's Thesis

22 ECTS

Master's Thesis | MT
Master's Thesis | MT
0 SWS 20 ECTS
Content
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
Teaching method
Independent work supported by coaching
Examination
Final exam: Seminar paper
Literature
Abhängig vom gewählten Thema
Teaching language
Englisch

20 ECTS

Master's Finals (2 ECTS of your choice)

Master's Finals - FH Campus Wien | AP
Master's Finals - FH Campus Wien | AP
0 SWS 2 ECTS
Content
- Presentation and discussion of the final thesis
- subject discussion
The defensio consists of the presentation and defence of the Master's thesis as well as an examination on its scientific environment and an examination covering a further examination subject from the compulsory modules which is to be substantially distinguished from the environment of the Master's thesis.
Teaching method
Independent development
Examination
Final exam: Master exam
Literature
Je nach Thema der Abschlussarbeit bzw. vorgegebene Literatur für die Prüfungsfragen
Teaching language
Englisch

2 ECTS

Master's Finals - Masterthesis Universität Wien | AP
Master's Finals - Masterthesis Universität Wien | AP
0 SWS 1 ECTS
Content
- Presentation and discussion of the thesis
This part of the defense consists of the presentation and defense of the Master's thesis.
Teaching method
Independent development
Examination
Final exam: Commissioned examination (Master's examination)
Literature
Je nach Thema der Abschlussarbeit bzw. vorgegebene Literatur für die Prüfungsfragen
Teaching language
Deutsch

1 ECTS

Master's Finals - Universität Wien | AP
Master's Finals - Universität Wien | AP
0 SWS 1 ECTS
Content
- Subject discussion
The defensio consists of an examination on the scientific context of the Master's thesis and an examination on another subject from the compulsory modules that is essentially distinct from the context of the Master's thesis.
Teaching method
Independent development
Examination
Final exam: Commissioned examination (Master's examination)
Literature
Je nach Thema der Abschlussarbeit bzw. vorgegebene Literatur für die Prüfungsfragen
Teaching language
Deutsch

1 ECTS

Module Research Design and Academic Writing

2 SWS

4 ECTS

Master Colloquium | SE
Master Colloquium | SE
2 SWS 4 ECTS
Content
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
Teaching method
Group work, discussion, presentation, feedback, interactive lecture with practical exercises.
Examination
Continuous assessment: Oral presentation, written work in the form of an exposé.
Literature
Baur, N., & Blasius, J. (Eds.). (2014). Handbuch Methoden der empirischen Sozialforschung. Wiesbaden, Germany: Springer VS.
Flick, U. (2011). Qualitative Sozialforschung: Eine Einführung. Reinbek bei Hamburg: Rowohlt.
Hug, T., & Poscheschnik, G. (2014). Empirisch forschen (Vol. 3357). UTB.
Häder, M. (2010). Empirische Sozialforschung. Wiesbaden: VS Verlag für Sozialwissenschaften.
Mayring, P. (2010). Qualitative Inhaltsanalyse. Grundlagen und Techniken, 11. Aufl. Beltz.
Kuckartz, Udo (2016) Qualitative Inhaltsanalyse. Methoden, Praxis, Computerunterstützung. Weinheim: Beltz Juventa.
Teaching language
Englisch

2 SWS

4 ECTS

Number of teaching weeks:
18 weeks per semester

Class Schedule at the FH Campus Wien:
Fridays (full day), occasionally Saturdays (full day).

Electives
Selection and participation according to available places. There may be separate admission procedures.

After graduation

As a graduate of this program, a wide range of occupational fields and career opportunities are open to you. Find out here where your path can take you.

These subject-specific competences as well as the acquired interdisciplinary and methodological competences qualify graduates for careers in the scientific as well as in the private sector. Depending on the personal specialization, various professional fields open up. The interdisciplinary character of the program qualifies students for various working areas: IT sector, consulting and human resources development.

language, translation and localization industry
language technology in the sense of language and text processing and translation technology
transcultural knowledge organization
language resource management
machine translation

multilingual product management
multilingual information processing
multilingual human-computer interaction
usability and data science

Studying made easy

Buddy Network

Support for getting started in Engineering

Extension curriculum

For Multilingual Technologies, Master

Funding & Scholarships

More information here

Time abroad

Expertise, language skills, broadening horizons

Open Lectures

Center for academic writing

Intensive German course

Start-up Service

Doctoral Service

Validation

Accessibility

queer @ FH Campus Wien

Networking with graduates and organizations

We work closely with renowned companies in commerce and industry, with universities, institutions and schools. This guarantees you contacts for employment or participation in research and development. In the course of exciting school cooperations, students may contribute to firing up pupils on topics such as our Bionics Project with the Festo company. You can find information about our cooperation activities and much more at Campusnetzwerk. It's well worth visiting the site as it may direct you to a new job or interesting event held by our cooperation partners!

Contact

Head of Degree Program

FH-Prof. DI Dr. Igor Miladinovic

Head of Degree Programs Computer Science and Digital Communications, Multilingual Technologies, Software Design and Engineering
+43 1 606 68 77-2131
igor.miladinovic@fh-campuswien.ac.at

Secretary's office

Mgr. Andrea Slaminka

Favoritenstraße 226, B.3.05
1100 Wien
+43 1 606 68 77-8455
+43 1 606 68 77-2139
mlt@fh-campuswien.ac.at

Map main campus Favoriten (Google Maps

Semester- Office hours:

Monday 10:00 AM - 12:00 PM and 1:00 PM - 3:00 PM
Wednesday 10:00 AM - 12:00 PM and 1:00 PM - 3:00 PM
Friday 10:00 AM - 12:00 PM and 1:00 PM - 3:00 PM

Master's Degree Program

Multilingual Technologies

Multilingual Technologies