Course catalogue doctoral education - VT22

  • Application can be done between 2021-10-15 and 2021-11-15
Application closed
Title Artificial Intelligence and Machine Learning for Biomedical and Clinical Research
Course number 5223
Programme Cell Biology and Genetics
Language English
Credits 3.0
Date 2021-11-22 -- 2021-12-03
Responsible KI department Department of Microbiology, Tumor and Cell Biology
Specific entry requirements At least 1,5 credits from a course in basic statistics.
Purpose of the course To increase knowledge about Machine Learning (ML) and Artificial Intelligence (AI) applications in biological and medical research, introduce first-hand experience and skills with different frameworks. The course requires no preliminary programming skills as well as no preliminary expertise in ML and AI. This course is given at a basic/novice level with no expertise in ML/AI and preliminary programming skills required, though experience in data analysis using RStudio/MatLab or similar analytic environment is an advantage.
Intended learning outcomes After the completed course, the participants will be able to describe and discuss general aspects of ML and AI in a biomedical or medical context including ethical dilemmas and challenges. Practically, they should be able to prepare and analyse different data types related to own research, such as texts, omics, genomic sequences, images etc. using a range of ML and AI exploration and classification techniques as well critically analyse the outcome and estimate performance.
Contents of the course Basic information about AI and ML, multivariate dataset preparation, classic methods of univariate and multi-dimensional analysis (Principal Component Analysis, Linear Discrimination Analysis, Factor Analysis), variable selection and sparse regression models (lasso regression, ridge regression, elastic net), supervised and unsupervised learning with neural networks, federated learning, performance estimation methods.
Teaching and learning activities The course consists of lectures, group discussions, and hands-on labs. Previous experience from practical experience applying modelling in a computer-based environment (e.g. in R, SAS, STAT, Matlab or Python), is strongly recommended.
Compulsory elements All planned activities including lab and group works are mandatory. Absence has to be compensated with a report on the lab work, which student will have to do.
Examination The student will be examined by their (a) labs accomplishment (b) final project report and (c) written reviews of projects of 2 other students.
Literature and other teaching material Both classic and up-to-date articles and websites will be recommended.
Number of students 8 - 8
Selection of students Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) start date of doctoral studies (priority given to earlier start date)
More information The course takes place at Campus Solna. It is full time and intensive. For any questions about course contents and practicals, email who is the main teacher of the course.
Additional course leader The course co-director if Iurii Petrov,
Latest course evaluation Course evaluation report
Course responsible Andrey Alexeyenko
Department of Microbiology, Tumor and Cell Biology
Contact person Matti Nikkola
Institutionen för cell- och molekylärbiologi