Machine Learning and AI Engineering

Are you fascinated by how machines can learn from data, recognise patterns, and make intelligent decisions? Do you want to work with the technologies behind self-driving cars, recommendation engines, generative AI, and intelligent data systems? Then the specialisation in Machine Learning and AI Engineering is for you.

In this specialisation, you will explore the full data-to-intelligence pipeline — from mathematical modelling and statistics to data visualisation and machine learning. You will gain hands-on experience analysing data, designing predictive models, and implementing AI systems that solve real-world engineering problems.

You will learn how to visualise data effectively, understand the mathematics that powers modern AI, and apply machine learning methods responsibly and ethically. This specialisation prepares you for a future where data and artificial intelligence drive innovation across industries — from automation and robotics to digital services and smart technologies.

About the specialisation in Machine Learning and AI Engineering

  • We are surrounded by intelligent systems — from recommendation engines and voice assistants to autonomous vehicles and generative AI tools that create text, images, and designs.

    As a software engineer specialising in Machine Learning and AI Engineering, you will learn how to design, train, and apply models that enable machines to learn from data and make decisions. You will combine mathematics, statistics, and programming to build intelligent systems that can analyse information, recognise patterns, and support automation and innovation in modern engineering.

    The specialisation includes subjects such as:

     

    MAL2 – Machine Learning for Artificial Intelligence

    Building on the foundation from the mandatory Machine Learning course, Machine Learning for Artificial Intelligence takes you deeper into the field. You will explore advanced methods such as neural networks, deep learning, generative AI, and reinforcement learning, while gaining practical experience in developing, training, and fine-tuning intelligent models.

    ASE – Statistics and Data Analysis

    In Statistics and Data Analysis, you will work with real-world data to understand patterns, test hypotheses, and communicate insights that support engineering and business decisions.

    SMP – Stochastic Modelling and Processes

    In Stochastic Modelling and Processes, you will learn about probability theory and random systems, and how these can be modelled and simulated using Python. The course forms a bridge between theory and practice in data-driven modelling.

    VIZ – Data Visualisation

    In Data Visualisation, you will learn how to transform complex datasets into meaningful and engaging visual representations. You will gain practical experience designing interactive visualisations using D3.js and learn to communicate insights clearly and effectively.

    ALI – Applied Linear Algebra

    In Applied Linear Algebra, you will gain the mathematical foundation for modern AI. You will learn about vectors, matrices, and transformations, and see how these concepts power technologies such as computer vision, recommendation systems, and deep learning.

    BUI – Business Intelligence and Analytics

    In Business Intelligence and Analytics, you will learn how to structure and analyse large amounts of data to support decision-making, using professional BI tools and data pipelines.

    Together, these courses will prepare you to work in one of the fastest-growing fields in technology — where software meets data, and machines learn to think.

  • With the specialisation in Machine Learning and AI Engineering, you will be qualified for positions such as:

    • Machine Learning Engineer
    • Data Scientist
    • AI Engineer
    • Data Analyst
    • Business Intelligence Developer
    • Software Engineer (AI/ML focus)
    • Computer Vision or NLP Engineer
    • Research and Development Engineer (AI applications)
  • You are eligible for the specialisation if you are a student on the software engineering educational programme at VIA in Horsens and have completed the first 5 semesters. See the software engineer admission requirements.

    Student counselling

    Contact our study counsellor if you would like to know more about your admission possibilities:

    E: eng.studycounselling@via.dk

    Office hours: Monday, Tuesday and Thursday 9:00-12:00 and Friday 10:00-12:00.

    If you have questions about the academic content of the specialisation, please contact lecturer Richard Brooks rib@via.dk

Meet students and graduates