Introduction to AI - practical applications
3 credits, course, bachelor's level, D0052E
Summer 2026
AI becomes valuable when it solves real problems.
“Introduction to AI – Practical Applications” turns concepts into practice across domains such as healthcare, media, manufacturing, public services, and education. You will learn to scope an AI opportunity, choose appropriate techniques, and assemble a working prototype that is ethical, robust, and explainable. Through application-driven modules, you’ll explore data preparation, prompt and tool design, workflow orchestration, and evaluation—then apply them in a project that matters to you. Live sessions focus on feedback and road-mapping, while self-paced materials let you learn whenever it suits your schedule. We also cover governance and sustainability: how to document your solution, manage risks, and plan for maintenance. With 3 ECTS, three live sessions, and a project-based examination, this course is ideal for learners who want to build something real and useful in a short time. Prerequisites are light (completion of “Introduction to AI – Basic” or similar), and teaching is in English. Whether you aim to streamline a process, prototype a research assistant, or pilot a small AI service, you’ll emerge with a portfolio-ready artefact and the confidence to take your idea from sketch to deployment-ready plan. Expect a supportive, collaborative environment where practical decisions—data, tools, and evaluation—are explained clearly and grounded in real-world constraints.
You will learn
Who is it for? Learners who want a convincing proof-of-concept in weeks.
You will learn
- Scoping opportunities, defining success, choosing feasible techniques.
- Building ethical, explainable workflows with modern tools and evaluations.
Who is it for? Learners who want a convincing proof-of-concept in weeks.
3 credits, course, bachelor's level, D0052E
Summer 2026
AI becomes valuable when it solves real problems.
“Introduction to AI – Practical Applications” turns concepts into practice across domains such as healthcare, media, manufacturing, public services, and education. You will learn to scope an AI opportunity, choose appropriate techniques, and assemble a working prototype that is ethical, robust, and explainable. Through application-driven modules, you’ll explore data preparation, prompt and tool design, workflow orchestration, and evaluation—then apply them in a project that matters to you. Live sessions focus on feedback and road-mapping, while self-paced materials let you learn whenever it suits your schedule. We also cover governance and sustainability: how to document your solution, manage risks, and plan for maintenance. With 3 ECTS, three live sessions, and a project-based examination, this course is ideal for learners who want to build something real and useful in a short time. Prerequisites are light (completion of “Introduction to AI – Basic” or similar), and teaching is in English. Whether you aim to streamline a process, prototype a research assistant, or pilot a small AI service, you’ll emerge with a portfolio-ready artefact and the confidence to take your idea from sketch to deployment-ready plan. Expect a supportive, collaborative environment where practical decisions—data, tools, and evaluation—are explained clearly and grounded in real-world constraints.
You will learn
Who is it for? Learners who want a convincing proof-of-concept in weeks.
You will learn
- Scoping opportunities, defining success, choosing feasible techniques.
- Building ethical, explainable workflows with modern tools and evaluations.
Who is it for? Learners who want a convincing proof-of-concept in weeks.
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