Course description
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.
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.
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.
Course contents: 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.
Prerequisites and Selection
Prerequisite courses, or equivalent
At least 1,5 credits from a course in basic statistics.
Selection
Selection will be based on:
1) the relevance of the course syllabus for the applicant’s individual study plan/research (according to written motivation).
2) start date of doctoral studies (priority given to earlier start date).
Course director
Iurii Petrov
Course syllabus
C1F5223
Department
Department of Microbiology, Tumor and Cell Biology
Doctoral programme
Cell Biology and Genetics (CBG)
Type of course
**Other course
Keywords
Artificial Intelligence , Machine Learning, Bioinformatics