Introduction to AI - Reasoning
1,5 credits, course, bachelor's level, D0051E
Summer 2026
How do AI systems reason about the world, justify their conclusions, and explain their decisions?
“Introduction to AI – Reasoning” explores the foundations of symbolic and knowledge-based AI—logic, rules, constraints, search, planning, and knowledge representation—alongside modern, hybrid approaches. You’ll learn to formalise problems, design reasoning pipelines, and evaluate correctness and explainability, with an emphasis on transparent decision-making in safety-critical or regulated contexts. Through demonstrations and hands-on exercises, we connect reasoning to practical use cases in diagnostics, compliance, scheduling, and recommendations, showing when reasoning outperforms pure pattern-matching. The course is self-paced, taught in English, and includes two live sessions for questions and peer discussion. Assessment combines a reflective report with a compact mini-project, so you can apply reasoning to your own domain. Recommended background is “Introduction to AI – Basic” or similar. In 1.5 ECTS, you will gain mental models and practical tools for building AI systems that are not just accurate, but also trustworthy, auditable, and easier to maintain—skills increasingly demanded across European industries and public services. We also discuss how reasoning complements machine learning, how to combine rules with learned models, and how to make trade-offs between performance and interpretability. By the end, you will know when to reach for reasoning methods, how to implement them responsibly, and how to communicate their advantages to stakeholders.
You will learn
Who is it for? Learners who need transparent, auditable AI.
You will learn
- Core methods: logic, search, constraint solving, planning.
- Knowledge modelling with rules, ontologies, graphs.
- Evaluating correctness, explainability, maintenance; hybrid ML+reasoning.
Who is it for? Learners who need transparent, auditable AI.
1,5 credits, course, bachelor's level, D0051E
Summer 2026
How do AI systems reason about the world, justify their conclusions, and explain their decisions?
“Introduction to AI – Reasoning” explores the foundations of symbolic and knowledge-based AI—logic, rules, constraints, search, planning, and knowledge representation—alongside modern, hybrid approaches. You’ll learn to formalise problems, design reasoning pipelines, and evaluate correctness and explainability, with an emphasis on transparent decision-making in safety-critical or regulated contexts. Through demonstrations and hands-on exercises, we connect reasoning to practical use cases in diagnostics, compliance, scheduling, and recommendations, showing when reasoning outperforms pure pattern-matching. The course is self-paced, taught in English, and includes two live sessions for questions and peer discussion. Assessment combines a reflective report with a compact mini-project, so you can apply reasoning to your own domain. Recommended background is “Introduction to AI – Basic” or similar. In 1.5 ECTS, you will gain mental models and practical tools for building AI systems that are not just accurate, but also trustworthy, auditable, and easier to maintain—skills increasingly demanded across European industries and public services. We also discuss how reasoning complements machine learning, how to combine rules with learned models, and how to make trade-offs between performance and interpretability. By the end, you will know when to reach for reasoning methods, how to implement them responsibly, and how to communicate their advantages to stakeholders.
You will learn
Who is it for? Learners who need transparent, auditable AI.
You will learn
- Core methods: logic, search, constraint solving, planning.
- Knowledge modelling with rules, ontologies, graphs.
- Evaluating correctness, explainability, maintenance; hybrid ML+reasoning.
Who is it for? Learners who need transparent, auditable AI.
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