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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 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. It will be a guided tour of a useful, interesting, and important landscape, pointing out theoretical and application-oriented gold-mines along the way. The time schedule for the course is available here.
The course is structured around four labs.
Lab 0: Getting started
Get access to necessary cloud computing infrastructure and tools. The students are pointed to resources for gaining the necessary background in programming (Python and friends) and machine learning. The students will be encouraged to go through the material in Lab 0 on their own before the first gathering in the course.
Lab 1: Sequences and tabular data
Topics will include applications in e.g. medical health records and single-cell multi-omics, using classical machine learning and deep learning (e.g. natural language processing techniques, embeddings, sequence-to-sequence mappings).
Lab 2: Images in time and space
Topics will include live-cell imaging, MRI and video sequences (in behavioral and biological surveillance), using e.g. supervised and unsupervised classification and convolutional neural networks for image segmentation, classification, object detection, and regression.
Lab 3: Drug discovery, RRI and ethics
Topic will include scoring functions for docking and drug-target interactions, quantitative structure-activity/structure-property relationship (QSAR/QSPR), and de novo structure generation, using classical machine learning and "deep learning". The lectures associated to the lab will also deal with topics from responsible research and innovation (RRI), ethics, and "explainable AI", topics that are important for all applications of machine learning.
Team project
The students in the course will be divided into teams (aiming for a combination of "wet lab" and "dry lab" members in each team), working on a project related to the content and techniques presented in the course.
Individual PhD project pitch
As part of the course, the participants will pitch an idea/sketch of a how to use AI/ML in their own PhD project, or for a closely related area. E.g. a PhD candidate working on single-cell RNA sequencing or CITE-seq in her PhD project presents how deep learning could be used in the integrative analysis, pointing to literature or concretely digging into it. The students should produce a short, condensed written summary, and give an oral, plenary speed-presentation (5-7 mins) for their fellow students.
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 will also be familiar with challenges and limitations of applied machine learning.
Learning outcomes
Knowledge
- Able to explain the fundamental concepts of machine learning.
- Able to explain how machine learning can be used to solve practical problems in biological and biomedical domains.
- 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.
- An appreciation for the importance of open science, reproducible research, and of the role of competitions and challenges in modern data science-based research.
Skills
- Can 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 of crossdisciplinary 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:
- Team project
- Individual project pitch
- Individual digital exam
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.
Contact
Course organizer: Alexander Selvikvåg Lundervold, alexander.selvikvag.lundervold@hvl.no
Research school: Liv Eggset Falkenberg, liv.falkenberg@ntnu.no