Recommended prerequisite for participation in the module

The module builds on knowledge, skills and competences obtained in the course module: Data flow, Databases and Data Quality or knowledge, skills and competences equivalent to this.

Content, progress and pedagogy of the module

This course provides fundamental knowledge and skills in artificial intelligence, with a focus on machine learning techniques. The course offers students an understanding of basic concepts in AI and equips them with tools to work with machine learning systems. An important element is also to go through the entire machine learning process, from data preparation to model training, fine-tuning, and maintaining the finished system. By combining theory and practical exercises, a foundational knowledge in AI and ML will be built, serving as a basis to apply this knowledge in both health-related research and industrial contexts. 

Learning objectives

Knowledge

• Basic elements and structure of a machine learning system. 

• How patterns can be described using features. 

• Various health-related contexts and scenarios in which machine learning is involved. 

Skills

• Can apply and evaluate basic supervised and unsupervised machine learning models. 

• Can analyze, visualize, and select features. 

• Can prepare data for training, validation, and testing of machine learning models. 

Competences

• Can translate knowledge of features and basic AI models into the design, development, and evaluation of a simple AI system. 

• Can assess the applicability of prediction and classification approaches in health care contexts. 

Type of instruction

The teaching format is blended learning based on self-study of both written material and video clips, discussion in study groups and online seminars.

Exam

Exams

Name of exam	Machine Learning in the Welfare Sector
Type of exam	Written or oral exam
ECTS	5
Permitted aids	See semester description
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures