VT2022, 1.5 ECTS or 6 ECTS
Artificial intelligence (AI), and deep learning in particular, is rapidly entering medicine and life science research as well as pharmaceutical industry and health care institutions. One of the areas it is applied to is the analysis or generation of text and language data, for example to extract information from patient records or diagnostic questionnaires, summarize research literature, automate journalling, operate medical chat bots or gain public health insights from social media.
This course teaches how deep learning-based natural language processing (NLP) models can be used for text and speech analysis and highlights concrete application opportunities and challenges within medicine and life sciences. It also teaches computational skills and strategies for training NLP AI models. Even though the examples used in the course will be from medicine and life sciences, it is also relevant for others who want to learn about NLP. The course can be taken as follow up to the course “Artificial Intelligence in Medicine and Life Science – Introduction (NTF014F)” or as a free-standing option.
The course will mix lectures, hands-on computational exercises, Q and A sessions and research-related parts.
The first part of the course will be taught as a block, which counts as 1.5 ECTS. It is possible to extend this with a 4.5 ECTS optional project work over three weeks following the first block.
Several days in spring 2022. If you are unable to attend all days, you may still be able to participate in the course.
Optional project work required for additional 4.5 ECTS.
All teaching will be online.
Course organiser: Sonja Aits
Teachers: Sonja Aits, Marcus Klang
- Basic programming skills in Python
- Basic understanding of AI concepts (e.g. COMPUTE course "Artificial Inteligence in Medicine and Life Science - Introduction", NTF014F or equivalent self studies)
- Standard computer/laptop with internet connection
- It is typically not possible to admit MSc students to COMPUTE courses.
If you lack the pre-requisites self-study material prior to the course can be provided. No prior knowledge in medicine and life science is required to take part in the course.
Registration closed 7th November 2021 (firm)