Inhalt

[ 993TASMKPLV22 ] VL (*)Knowledge Representation and Learning

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Artificial Intelligence Johannes Fürnkranz 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Artificial Intelligence 2023W
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.
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
Beurteilungskriterien (*)Exam at the end of the semester
Lehrmethoden (*)Lectures with Slides
Abhaltungssprache Englisch
Literatur (*)Lecture Slides, Pointers to relevant literature are given in the lecture
Lehrinhalte wechselnd? Nein
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)
Präsenzlehrveranstaltung
Teilungsziffer -
Zuteilungsverfahren Direktzuteilung