Recommended prerequisite for participation in the module

Basic knowledge about Python is recommended, including running Python scripts and basic data structures. This can be obtained, e.g., in online material that will be made available to the students.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Computational-intensive techniques for evaluating models and estimation of variances (cross-validation and bootstrap) and has knowledge about the variance-bias trade-off.
• Understands the difference between supervised and unsupervised methods and between classification and regression.
• Machine Learning (ML) methods for classification (e.g., decision trees, k-nearest neighbours, and/or Bayes classifiers) and clustering (e.g., k-means clustering and/or Gaussian mixtures).
• Model averaging, bagging, and boosting as well as their impact on AI-driven predictive modelling.

Skills

• Can identify and discuss the strengths and weaknesses of different AI and ML techniques in relation to a specific analysis task.

Competences

• Assess the applicability of AI and ML techniques in a given situation.
• Use correct professional terminology.
• Acquire additional knowledge in the field.

Type of instruction

Types of instruction are listed at the start of §17; Structure and contents of the programme.

Extent and expected workload

Expected module workload is 150 hours.

Exam

Exams

Name of exam	Introduction to AI Techniques
Type of exam	Oral exam
ECTS	5
Permitted aids	Please see the module description in Moodle.
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures