| Artificial Intelligence [2026 SoSe] | ||
|---|---|---|
| Code IAI |
Name Artificial Intelligence |
|
| LP 8 |
Dauer one semester |
Angebotsturnus irregular |
| Format Lecture 4 SWS + Exercise course 2 SWS |
Arbeitsaufwand 240h; thereof 90h lectures and tutorials 15h exam preparation 135h lecture wrap-up and homework |
Verwendbarkeit B.Sc. Informatik |
| Sprache English |
Lehrende Daniel Gnad |
Prüfungsschema |
| Lernziele | Students can - explain the differences between symbolic reasoning methods and data-driven learning methods - model problems in the covered reasoning formalisms - apply reasoning algorithms to simple problems - describe basic machine learning concepts - apply learning techniques to train simple models |
|
| Lerninhalte | - problem solving as search - general game playing - knowledge representation and reasoning - constraint programming - logic programming - automated planning - machine learning basics |
|
| Teilnahme- voraus- setzungen |
recommended are: Einführung in die Praktische Informatik, Algorithmen und Datenstrukturen, Einführung in die Theoretische Informatik | |
| Vergabe der LP und Modulendnote | The module is completed with a graded oral or written examination. The final grade of the module is determined by the grade of the examination. The requirements for the assignment of credits follows the regulations in section modalities for examinations. | |
| Nützliche Literatur | Russell & Norvig: Artificial Intelligence: A Modern Approach | |