Overview
The BTS Applied Artificial Intelligence curriculum is designed to prepare students for all steps in the deployment cycle of AI systems. To this end, graduates will be able to work efficiently in a team environment and to uphold a conversation on technical topics, analysing core business requirements and activities, while also considering ethical and legal implications.
Students engage in a dynamic learning journey that includes a variety of teaching methods such as lectures, tutorials, self-guided study, individual projects, collaborative group projects, cross-course projects, interdisciplinary initiatives, and seminars. In addition to these educational activities, students have the unique opportunity to participate in private and public events, facilitating connections with professionals in the national and international AI industry.
Course content
| Teaching subject | ||||||
| Educational Component (Module) | Semester 1 | Semester 2 | Semester 3 | Semester 4 | ||
| Course* | ECTS | ECTS | ECTS | ECTS | ||
| Building Blocks of Intelligent Systems | ||||||
| Building Blocks of Intelligent Systems 1 | ||||||
| Introduction to Artificial Intelligence | 3,5 | |||||
| Training of AI Systems 1 | 3,5 | |||||
| Programming for AI 1 | 7 | |||||
| Mathematics for AI Professionals 1 | 3 | |||||
| Building Blocks of Intelligent Systems 2 | ||||||
| Training of AI Systems 2 | 3,5 | |||||
| Programming for AI 2 | 4 | |||||
| Mathematics for AI Professionals 2 | 3 | |||||
| Building Blocks of Intelligent Systems 3 | ||||||
| Training of AI Systems 3 | 4 | |||||
| Programming for AI 3 | 3,5 | |||||
| AI Projects | ||||||
| AI Projects 1 | ||||||
| Application Domains of Artificial Intelligence 1 | 3 | |||||
| Project Management 1 | 2 | |||||
| Domain-specific Project 1 | 3,5 | |||||
| AI Projects 2 | ||||||
| Application Domains of Artificial Intelligence 2 | 3 | |||||
| Project Management 2 | 2 | |||||
| Domain-specific Project 2 | 5 | |||||
| AI Projects 3 | ||||||
| Final Project | 10 | |||||
| AI Operations |
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| AI Operations 1 | ||||||
| Operating Systems and Servers for AI | 2 | |||||
| Data Operations 1 | 2,5 | |||||
| Data Management Systems 1 | 3,5 | |||||
| AI Operations 2 | ||||||
| Development Operations 1 | 2,5 | |||||
| Data Management Systems 2 | 3,5 | |||||
| AI Operations 3 | ||||||
| Data Operations 2 | 2 | |||||
| Development Operations 2 | 2 | |||||
| Data Management 3 | 3,5 | |||||
| Ethics and Governance |
||||||
| Ethics and Governance 1 | ||||||
| Ethical Decision Making | 2 | |||||
| Entrepreneurship | 3 | |||||
| Ethics and Governance 2 | ||||||
| Ethics and Social Implications of AI | 3 | |||||
| Legal and Regulatory Frameworks for AI 1 | 2 | |||||
| Ethics and Governance 3 | ||||||
| English for AI Professionals | 3 | |||||
| Legal and Regulatory Frameworks for AI 2 | 2 | |||||
| Internship |
||||||
| Internship | ||||||
| Internship | 20 | |||||
ECTS: European Credit Transfer and Accumulation System
Learning outcomes
Graduates will be able to participate in the planning, configuration, integration, automation, and maintenance of AI systems. To that end, graduates will have developed diverse competencies throughout the BTS curriculum. Among other things, they will be able:
- to use programming techniques to solve AI-driven problems;
- to define, plan, and deploy a solution, local or remote, for an AI-based or AI-adjacent problem;
- to identify, select, and use existing AI-models to solve a business problem;
- to use, refurbish, or combine existing systems and solutions;
- to process, fuse, merge and aggregate data;
- to analyse core business requirements and activities;
- to recognise the ethical and societal implications of AI solutions;
- to operate within and strive to uphold laws, regulations, and good practices.
Professional profiles
Graduates will be qualified to hold various positions within a team environment and to perform tasks as diverse as the following:
- Data preparation and management
- AI system maintenance and monitoring
- Hardware setup and configuration
- Support for AI model development
- Running experiments and tests
- Assisting in model training
- Integration tasks
This skill set is suited for, but not limited to, profiles such as Data Steward, Machine Learning Technician, and Data Infrastructure Technician.