A hands-on course on artificial intelligence in computational biotechnology and medicine

This course provides a practical, project-based, hands-on exploration of the state-of-the-art techniques and software frameworks from machine learning and deep learning for solving real-world problems from biomedicine, biotech, and related fields. 

Register here

About the course

Recent years have seen a surge of interest in machine learning and artificial intelligence. This is caused by highly-visible breakthroughs in a variety of areas like computer vision, natural language processing, speech recognition and synthesis, and in the analysis of unstructured, tabular data. As many of the challenges faced in more general domains are transferable to specialized domains, we have seen a torrent of scientific publications and new applications across all data-driven fields, from medicine to physics, biology to cosmology. The course provides a practical, project-based, hands-on exploration of state-of-the-art techniques and soft-ware frameworks from machine learning and deep learning for solving real-world problems from biomedicine, biotech and related fields. It will be a guided tour of a useful, interesting and important landscape, pointing out theoretical and application-oriented gold-mines along the way. 

By the end of the course the participants will have a solid understanding of the ideas of machine learning, and good experience and practical intuitions around how the methods can be applied in biomedical domains, using modern tools and frameworks. They'll also be familiar with some challenges and limitations of applied machine learning.

The course is structured around two modules, project pitches and a team project.

Block 0: Getting started (until April 25th)

Getting started. Description of course and how they should work with the code repository, project pitch and the team project. The students will have access to a lot of useful material to get up to speed with both the programming frameworks and the background knowledge required by the course.

Block 1: Online (April 25th - May 1st)

Motivation lectures (motivates the course topics, structure and philosophy).

Getting started on the team project

Block 2: Physical meeting (May 2nd - May 4th)

Lectures related to Module 1 on deep learning
Hands-on work on Module 1 on deep learning
Plenary, individual pitch of project (speed-presentation of about 5-7 mins)
Discussions about the team project
Social activity

Block 3: Online (May 9th and 10th):
Lectures and hands-on work related to Module 1 on deep learning and Module 2 on biomedical imaging
in time and space

Block 4, online (June 1st  & June 7th)
June 1st: Team project presentations
Feedback on team project
June 7th: Oral exam (pass/fail)

Learning outcomes

After completion of the course you will:


  • Be able to explain the fundamental concepts of machine learning and deep learning
  • Be able to explain how deep learning can be used to solve practical problems in biological and biomedical
  • Be able to explain the limitations and challenges involved in using machine learning in biology, biomedicine and
    biotech, and related societal challenges. An understanding of the concepts Explainable AI, and RRI
  • Be able to explain some fundamental principles of biomedical imaging
  • Have an appreciation for the importance of open science, reproducible research, and of the role of competitions
    and challenges in modern data science-based research


  • Be able to find and use modern software tools for data analysis, visualization and reporting (e.g. figure / graphics
    production with Jupyter notebooks).
  • Transdisciplinary communication: can communicate selected methods and software packages where these
    methods are implemented and explain their relevance to medical research and clinical practice.

General competence

Acknowledging the importance of mathematical models and computations in the analysis and understanding of complex biological systems, and the need for cross-disciplinary collaborations in future biotech and medicine.

Course evaluation

The course is worth the equivalence of 5 ECTS. If you need the ECTS for the course to be part of the academic training in their PhD, you must apply to your home institution in advance to get it approved. The Western Norway University of Applied Sciences will provide a course certificate when you pass the course.

The students will be evaluated based on three activities:

  • Project pitch (must be passed)
  • Team project (must be passed)
  • Oral exam (pass/fail)

Recommended prerequisites

The course is aimed at the subset of DLN research school members that already have some experience in programming with Python, R or MATLAB, and a strong interest in machine learning and artificial intelligence. Participants from methodological fields like computer science, statistics, and mathematics are welcome to join the course, as long has they have some familiarity with biological or biomedical data and a strong interest in biotech or medicine.


Course organizer: Alexander Selvikvåg Lundervold

Research school: Rosalie Zwiggelaar

Published Jan. 21, 2022 3:53 PM - Last modified Feb. 16, 2022 8:34 PM