Welcome to the Artificial Intelligence Academy
Shape the future with advanced artificial intelligence skills.
Step into the world of artificial intelligence with the AI Academy, where learning meets innovation and strategy. Designed in coherence with Luxembourg’s National Artificial Intelligence and Data Strategies and in synergy with the national AI Factory initiative, this programme empowers learners to play an active role in shaping the country’s digital and technological future.
Through hands-on training, real-world projects, and expert mentorship, the AI Academy equips participants with the practical skills and strategic insight needed to thrive in one of the most transformative fields of our time. Whether you are starting your career or seeking to specialise, you will gain the expertise and confidence to harness intelligent systems responsibly and drive innovation across industries.
With career pathways leading to roles such as data scientist, machine learning specialist, or AI designer, the AI Academy develops the sovereign digital talent needed to strengthen Luxembourg’s technological competitiveness. By cultivating the next generation of AI professionals and innovators, it directly supports the government’s vision of artificial intelligence as a pillar of sovereign competitiveness and a key driver of the country’s digital transformation.
Getting started
To join the AI Academy, candidates should demonstrate a solid foundation in mathematics, statistics, and Python programming. These prerequisites ensure learners are well-prepared to begin advanced AI training with confidence. If candidates lack some of these skills, they will have the opportunity to complete refresher courses to maximize their chances of successfully completing the programme. Joining the AI Academy begins with a simple but essential evaluation process designed to ensure candidates are ready for advanced AI training.
The first step is a 30-minute online test featuring 30 multiple-choice questions that assess your understanding of core IT concepts. This is followed by a 1-hour online project to demonstrate problem-solving and technical skills. You can find a preview of the assessment here. Once both steps are completed, selected candidates will be invited to an interview to discuss their background, motivation, and specialisation preferences: Data Science or Machine Learning.
Curriculum
Indicated time frames are based on a weekly time commitment of 40 hours. If you choose to pursue the programme with a weekly time commitment of 20 hours, the overall duration will double accordingly. Please note that the duration also varies depending on the specialisation you select.
Common Core Programme (2 months)
- Python syntax, data types, and structures
- Functions, loops, and conditionals
- File handling and exceptions
- Object-Oriented Programming
- Modules and Packages
- Scalars, Vectors and Matricess
- Matrix Operations
- Determinants
- Eigenvalues and Eigenvectors
- Limits and Derivatives
- Chain Rule and Partial Derivatives
- Integration basics
- Probability Distributions
- Mean, variance, and standard deviation
- Bayes’ theorem and conditional probability
- Pandas workflow
- Introduction to visualization
Machine Learning Specialisation (4 months)
- Regression models
- Classification models
- Neural Network Basics & Architectures
- Training & Optimization
- Regularisation Techniques
- Transfer Learning
- Autoencoders
- Generative Models
- Word embeddings
- Sequence Modeling (RNN, LSTM, GRU)
- Transformer architecture
- Fundamentals of Reinforcement Learning
- Value-Based Methods
- Policy-Based Methods
Data Science Specialisation (3 months)
- Data collection
- Exploratory Data Analysis
- Data Preprocessing
- Working with databases
- Regression and Classification overview
- Linear Models
- Tree-Based Models
- Model evaluation and tuning
- Pipeline building with Scikit-learn
- Clustering
- Dimensionality reduction
- Neural Networks
- Computer Vision
- Generative AI
Capstone Project (1 month full-time)
The capstone project is the culmination of your specialisation, designed to showcase your skills in real-world applications. It can be inspired by your own ideas or proposed during an internship, with mentors guiding you to bring your vision to life. Capstone project examples include:
- Analyse social media or streaming platform data to recommend trending content.
- Build a generative AI system that creates personalised visuals, music, or text content.
