B.Sc. Applied Artificial Intelligence (AAI) – overview
6 semesters, 180 ECTS, taught in English, with industry projects, an 18 ECTS internship and a Bachelor thesis.
Key facts
| Degree | Bachelor of Science (B.Sc.) |
|---|---|
| Duration | 6 semesters |
| Credits | 180 ECTS |
| Language | English |
| Assessments | Written exams, project work, presentations, practical work, case studies |
| Next steps | Qualifies for direct industry entry or specialised Master's programmes |
Competence roadmap
- Semesters 1–2 – Foundations: the mathematical and technical base (Python, maths, systems)
- Semesters 3–4 – Core disciplines: AI, machine learning and data engineering
- Semesters 4–5 – Application & specialisation: elective tracks, industry projects, professional internship
- Semester 6 – Synthesis: Bachelor thesis and research methodology
Phase 1: Technical foundation (semesters 1 & 2)
- Mathematics & logic: Mathematics for AI (linear algebra, calculus), statistics & probability
- Computer science essentials: computer systems, data structures & algorithms, software engineering, computer networks
- Data & AI intro: introduction to AI (history, search algorithms), foundations of data science
Goal: acquire the tools to translate mathematical formalisms into practical code.
Phase 2: Core AI competencies (semesters 3 & 4)
- Machine learning: supervised/unsupervised learning, decision trees, SVMs
- Deep learning: neural networks, CNNs, RNNs, model evaluation
- Data engineering: databases & big data (Hadoop, Spark), cloud computing & architectures
- Advanced interfaces: natural language processing, computer vision
Bridging theory and practice
- Collaborative Industry Project I: project management, stakeholder interviews and scope definition with real partners
- Collaborative Industry Project II: execution, model training and deployment of the solution
- Internship (18 ECTS): a full semester integrated into the curriculum, working within an organisation
- Bachelor thesis: independent research and development
Your future role
Machine Learning Engineer · AI Consultant · Data Scientist / Analyst · Prompt Engineer / GenAI Specialist · Robotics Specialist · AI Ops Engineer