AI for Medicine and Life Sciences - Introduction (1.5 ECTS, likely spring or fall term 2023)
Artificial intelligence is rapidly entering medicine and life science research as well as pharmaceutical industry and health care institutions. This introduction course will give an overview over artificial intelligence concepts and methods and over current and future applications of artificial intelligence in medicine and life sciences. Societal, ethical and legal challenges will also be addressed. In addition, students will hear about ongoing research in this area at Lund University and develop a project plan for an artificial intelligence research project in their own research domain. This is the first course in the new course package on "Artificial Intelligence in Medicine and Life Science" and will not have any programming exercises. It will be followed by several in-depth courses with practical exercises, each focusing on different types of data.
AI in Medicine and the Life Sciences – AI for text and language data (1.5/6.0 ECTS, likely fall term 2023)
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.
Monte Carlo and Molecular Dynamics Tools (7.5 ECTS, likely fall term 2023)
Introduction to Medical Bioinformatics (1.5 ECTS, likely fall term 2023)
Basic Data Handling and Visualization with R (1.5 ECTS, likely fall term 2023)
Reproducible and Interactive Data Analysis and Modelling using Jupyter Notebooks (4 ECTS, will most likely run again in 2024)
The aim of this course is to introduce students to the Jupyter Notebook which is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. Through the notebooks, research results and the underlying analysis can be transparently reproduced as well as shared.