AI, Machine Learning & Data

In this semester you will learn:

• to prepare structured and unstructured datasets on theoretical data quality standards by means of domain analysis and exploratory data analysis;
• to use findings from your data design to preprocess data, train and validate machine learning algorithms and evaluate the quality and usefulness of produced models for a defined domain;
• to deliver AI solutions that follow the three Explainable AI principles of transparency, interpretability, and explainability.

During this semester you work on individual challenges and an industry group project. Each one of these works has a different goal and different context, however, they all follow the same process in terms of business understanding, data design and predictive modelling.

Exercises

These will be used to get acquainted with new theory regarding the data design, model engineering and explainable AI.

Individual challenge

All obtained knowledge will be used to develop your own AI solutions. Multiple iterations will be built on the AI solution to improve the data quality, to apply different machine learning techniques and finetune them, and to apply and to align the requirements of the model with the stakeholder.

Industry project

An industry project will be provided by Fontys partners. You will investigate if and how predictive modelling can have an added value for their business process. Like the individual challenge, multiple iterations of the AI model will be developed to match the expectations of the Fontys partners and to improve the performance of the solution.

You can view the AI, Machine Learning & Data Canvas course.

You need to have completed any introduction semester to participate in AI, Machine Learning and Data.

After completing this semester, students can continue developing their machine learning skills in Enhanced AI Techniques (Advanced semester).

In Tilburg this semester is provided in combination with other topics.