650.020 (22W) Introduction to Artificial Intelligence
Überblick
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- Lehrende/r
- LV-Titel englisch Introduction to Artificial Intelligence
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 5.0
- ECTS-Anrechnungspunkte 8.0
- Anmeldungen 40 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Englisch
- LV-Beginn 03.10.2022
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Being able to explain what Artificial Intelligence (AI) is and what its major subfields are
Know major approaches (theory and methods) in AI, in particular Logic-based AI
Being able to apply some approaches to concrete settings
Lehrmethodik
Slides, workshops, exercises to prepare at home and present in class
Inhalt/e
In this course we present an overview of the field of artificial intelligence and provide a detailed account of some of its traditional areas. In particular, we will focus on knowledge representation and reasoning, give an introduction or reminder of logic and cover methods in heuristic search, which can be used for planning, constraint satisfaction, and similar tasks. These techniques usually require full knowledge or complete observability of the domain of discourse. We will then move towards settings with uncertainty and identify different sources of uncertainty. We will examine how to deal with an adversarial agent, for example in two-player games, will cover reasoning with incomplete knowledge and also cover probabilistic reasoning. Machine learning will be covered in detail in a separate course and will therefore only be touched on.
Prüfungsinformationen
Prüfungsmethode/n
There will be three online assessments and a mini-project.
Prüfungsinhalt/e
Topics covered in the module, but in particular logic, planning, constraint satisfaction, heuristic search, uncertain reasoning.
Beurteilungskriterien/-maßstäbe
Points obtained from assessments and project(s) will form the final grade. All assessments and project(s) should be delivered for passing.
Each of the four assessments will be marked numerically and normalized to 100 achievable grade points each. It will be necessary in principle to achieve at least half of the achievable grade points (200) and achieve more than half points on each assessment. We reserve the right to reward a condoned pass also if one assessment had less than half of the achievable points, but this will be reviewed on a case-by-case basis.
Constructive interaction in class can earn extra (bonus) points.
Grade points (x) to grades:
x < 200: Nicht Genügend (5)
200 <= x < 250: Genügend (4)
250 <= x < 300: Befriedigend (3)
300 <= x < 350: Gut (2)
350 <= x: Sehr Gut (1)
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
-
Fach: Artificial Intelligence
(Wahlfach)
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
- 650.020 Introduction to Artificial Intelligence (5.0h VC / 8.0 ECTS) Absolvierung im 1., 2. Semester empfohlen
-
Weitere LVen aus dem gewählten Spezialisierungsfach (
0.0h XX / 12.0 ECTS)
-
Fach: Artificial Intelligence
(Wahlfach)
- Masterstudium Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
-
Fach: Artificial Intelligence
(Pflichtfach)
-
2.1 Introduction to Artificial Intelligence (
0.0h VC / 8.0 ECTS)
- 650.020 Introduction to Artificial Intelligence (5.0h VC / 8.0 ECTS) Absolvierung im 1. Semester empfohlen
-
2.1 Introduction to Artificial Intelligence (
0.0h VC / 8.0 ECTS)
-
Fach: Artificial Intelligence
(Pflichtfach)