AI for Biomedical Data Analysis

Aim

Knowledge and Understanding

Upon completion of the course, students will be able to:

• Demonstrate an understanding of the principles underlying the application of Artificial Intelligence (AI) in biomedical data analysis.
• Explain and critically evaluate common AI methods and assess their appropriateness for different types of biomedical data.

Skills and Abilities

Upon completion of the course, students will be able to:

• Justify the selection of suitable AI methods based on a defined biomedical problem and the characteristics of the available data.

Judgement and Approach

Upon completion of the course, students will be able to:

• Critically analyze the limitations and challenges associated with applying AI to biomedical problems.

Content

The course introduces fundamental concepts and approaches in Artificial Intelligence (AI) as applied to biomedical data. This foundational understanding is used to justify the application of AI and to translate relevant formulations and concepts into AI-based analyses. Key topics include supervised and unsupervised machine learning, linear and non-linear model types, as well as validation and overfitting. In addition, practical tools are presented to provide an introduction to basic AI-driven analyses of biological data.