Modules CALM
Digital Communication Management
A strong foundation in digital communication and marketing is a key asset for language professionals. This course provides that foundation by digging deeper into the three digital media types -owned, earned and paid media- while covering key topics such as digital marketing, search engine optimization, social media campaigns, and web analytics. Students will not only analyze these topics in depth, but also learn to conceptualize and implement their own social media and digital marketing campaigns.
Digital Public Relations
prepares students to become professionals in today’s communication industry by introducing them to the most important topics regarding digital public relations. Students acquire both the knowledge and skills to strategically manage relationships with internal and external stakeholders. This course covers topics such as employee social media use, employee brand ambassadorship, authenticity on social media, the importance of dialogue and crisis communication.
Technical Writing
This course introduces the fundamentals of technical communication. Through hands-on sessions, students learn how to write technical documentation using the principles of clear, concise, and consistent writing while considering the target audience. Students also gain experience with industry-standard software, giving them a head start on the job market.
Machine Translation and Post-Editing
This comprehensive course provides students with a strong theoretical foundation in machine translation. It covers different machine translation architectures, evaluation methods, and the integration of MT into high-quality human translation workflows. Through hands-on sessions, students will learn to build customized MT systems and identify and tackle common post-editing challenges.
Terminology and Translation Technology
This course introduces the fundamental principles of terminology theory and terminology management. Students gain hands-on experience with industry-standard computer-based translation tools. They will critically reflect on the importance and limitations of technology and gain experience with terminology extraction, terminology management, translation memories, and computer-assisted translation tools, with or without machine translation integration.
Localisation
Localisation refers to the cultural adaptation and translation of digital content. This course covers key concepts and technical aspects of localisation. Students learn to localise different sources of digital contents (software applications, including user interfaces and online help files, websites, games and e-learning content).
Audiovisual Language Techniques
This course introduces techniques for various modes of audiovisual translation that enhance access to cultural content. Through hands-on sessions, students learn to create intralingual and interlingual subtitles and develop audio descriptions using professional software. Additionally, the course explores recent audiovisual translation technologies and scientific research driving automation in the field.
Computer-Assisted Language Learning
This comprehensive course provides students with a strong foundation in Computer-Assisted Language Learning (CALL). It explores recent technologies and development, including Web 2.0, mobile-assisted language learning, gamification, (L)MOOCS, and intelligent CALL. A significant part of the course is dedicated to exploring and critically accessing various electronic CALL applications, focusing on different language skills such as reading, writing, speaking, and listening.
NLP and linguistic analysis
This course provides a comprehensive introduction to the fundamentals of natural language processing (NLP). Students will explore techniques for analyzing text at multiple levels (morphological, syntactic, semantic, and discourse) to develop a deeper understanding of computational language understanding and generation. The course covers key theoretical concepts in language technology, such as language models and dynamic programming, alongside practical tasks like part-of-speech tagging, lemmatization, parsing, and named entity recognition.
Introduction to Language Processing with Phyton
This course offers an introduction to programming, focusing on automatic text processing. No prior programming knowledge is required. Python, a widely used programming language for natural language processing, serves as the primary tool.
Introduction to machine learning and feature engineering for NLP
Machine learning enables computers to learn from and make predictions based on data without being explicitly programmed. This course provides a comprehensive introduction to machine learning techniques and the role of feature engineering in building effective models. Students will explore fundamental machine learning principles, learn to extract meaningful features from data, and generate new ones through analysis. Hands-on exercises and real-world applications will help them develop practical skills to implement these techniques and improve predictive accuracy and model interpretability.
Neural networks and NLP applications
In recent years, artificial neural networks have excelled in a wide range of natural language processing tasks. Inspired by the human brain, these algorithms process input through interconnected neurons, passing through hidden layers to generate output. This course offers a comprehensive introduction to neural networks, covering key architectures, including the innovative transformer model and its components. The course also explores pre-trained large language models (LLMs) and fine-tuning strategies for specific NLP tasks. Through a blend of theory and hands-on practice, students will actively develop and experiment with Python code in each session.
Ethics for human-centered AI
This course raises awareness of the risks involved in building and implementing AI-systems and explores what ‘human-centered’ AI would look like. Each session addresses concerns raised by AI ethicists, such as environmental and financial costs, bias, stereotyping, data privacy, and copyright, along with existing or needed mitigation strategies. Grounded in current news, debates, and recent academic research, the course emphasizes fairness, accountability, and transparency in AI development. It also explores key challenges in human-centered design of AI products.