| Detailinformationen |
| Quellcurriculum |
Masterstudium Artificial Intelligence 2022W |
| Ziele |
(*)Students are familiar with the basic concepts of logic-based programming and databases. They know how commonly used knowledge representation formalisms such as semantic nets and the semantic web are rooted in formal logic. They are able to understand and devise automated methods for learning in such formalisms.
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| Lehrinhalte |
(*)Foundations of Logic Programming and Logic-Based Databases, Explanation-Based Learning, Inductive Logic Programming, Relational Learning, Foundations of Knowledge Representation, Semantic Networks, Semantic Web, Case-Based Reasoning, Fuzzy Logic
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| Beurteilungskriterien |
(*)Exam at the end of the semester
|
| Lehrmethoden |
(*)Lectures with Slides
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Lecture Slides, Pointers to relevant literature are given in the lecture
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| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)Until term 2022S known as: 993TARKKPLV21 VL Knowledge Representation and Learning
until term 2021S known as: 993TARKSAIV19 VL Symbolic AI
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| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
993TARKKPLV21: VL Knowledge Representation and Learning (2021W-2022S)
993TARKSAIV19: VL Symbolic AI (2019W-2021S)
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