The purpose of this elective is for the student to develop practical skills in applying
artificial intelligence, machine learning, and data science, to solve problems that can
not be solved using traditional software development methods

The student will gain knowledge on some of the most prominent areas of artificial intelligence:
• deep learning
• supervised learning: classification and regression
• unsupervised learning: cluster analysis, anomaly detection, prototype/archetype analysis
• reinforcement learning: Q-learning and Deep Q-learning
• appropriate use of performance metrics

The student will understand and be able to reflect on questions like:
• what is artificial intelligence
• what kind of problems can be solved with artificial intelligence, and which models should be applied

The student will gain skills to:
• solve practical problems using artificial intelligence and machine learning models
• identify and implement the most applicable models
• prepare data for use with machine learning models
• optimize models and solve common issues such as overfitting and underfitting
• apply performance metrics for model analysis
• communicate findings according to scientific standards

The student will learn to:
• develop solutions based on artificial intelligence that solves problems that can not be solved using traditional software development methods
• apply the scientific method to find solutions systematically
• formulate research questions, hypotheses, and null-hypotheses
• find and apply scientific articles for learning and to support own findings