BSc Artificial Intelligence

Overview

Statistical foundations, machine learning and responsible AI systems engineering. The programme combines scientific foundations with project-based engineering practice, delivered in structured semesters with supervised teaching.

Curriculum

Illustrative curriculum. The officially approved module structure will be published once programme documentation is available.

1. Semester 1

   • Python for AI
   • Mathematics for AI
   • Statistics
   • Data Structures

2. Semester 2

   • Algorithms
   • Machine Learning
   • Deep Learning
   • Data Processing

3. Semester 3

   • Natural Language Processing
   • Computer Vision
   • Generative AI
   • Large Language Models

4. Semester 4

   • Intelligent Agents
   • Reinforcement Learning
   • AI Ethics
   • AI Systems Engineering
   • Final Project

Learning outcomes

• Apply scientific method to technical problems
• Design, model and evaluate complex systems
• Communicate results to specialist and international audiences
• Assess technological developments in ethical and legal terms

Admission requirements

• Recognised secondary qualification or equivalent — higher education entrance qualification including mathematics and one natural science (overall grade 2.5 or better)
• Evidence of proficiency in the language of instruction — English C1 (IELTS 7.0 / TOEFL iBT 95) or German C1 (TestDaF 4x4 / DSH-2), depending on the language of instruction
• Complete application documentation — transcripts, language certificate, CV and identity document

Career opportunities

• Development and design roles in technology-driven organisations
• Research and development in institutes and industry
• Project and systems responsibility in interdisciplinary teams
• Progression to master's or doctoral study