The purpose of the elective is to provide students with practical skills and theoretical understanding of artificial intelligence, machine learning and data science. Students learn to identify problems that cannot be solved with traditional software techniques, as well as design and implement AI-based solutions.
Upon completion of the elective, the student is expected to be able to:
• Apply the principles behind artificial intelligence to solve relevant case-based
problems.
• Assess which types of AI models are most appropriate in specific situations.
• Work systematically with data collection, data preparation, model development and evaluation.

The student will gain knowledge about:
• Central problem areas within artificial intelligence, including discriminative AI, generative AI and metaheuristic search.
• The most widely used methods and algorithms within machine learning (including supervised, unsupervised, reinforcement learning, and genetic algorithms).
• Typical pitfalls in AI projects, such as the importance of data structures, ethical considerations and bias in data and models.
• Relevant evaluation methods, including performance metrics used to assess a
model's effectiveness.

In addition, the student will be able to reflect on questions such as:
• What defines artificial intelligence, and when should AI models be used instead of classical
software development?
• What type of data and model is required to solve a specific problem most effectively?
• How is it assessed and documented whether an AI model is 'good enough' in practice?

The student will develop skills to:
• Identify and implement relevant models (e.g. neural networks, decision trees,
clustering algorithms, etc.).
• Prepare and transform data (feature engineering, data scaling, handling of missing
values, etc.) for model training.
• Optimize models to handle overfitting and underfitting, including the use of
regularization methods, hyperparameter tuning and cross-validation.
• Use performance metrics to evaluate and compare different models.
• Communicate results and conclusions in a scientific and structured manner – both in written and oral form.

The student will acquire the skills to:
• Use artificial intelligence as a tool in research and development, including solving problems that are not possible to handle with traditional software.
• Plan and implement AI projects using the scientific method, from
problem identification and data collection to experimental evaluation.
• Formulate research questions, formulate hypotheses and evaluate results based on relevant null hypotheses.
• Incorporate results from scientific articles and relevant sources into their own projects, so that the students can substantiate solutions with documented knowledge.