621.062 (23W) Introduction to Artificial Intelligence 2
Überblick
- Lehrende/r
- LV-Titel englisch Introduction to Artificial Intelligence II
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 22 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 04.10.2023
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Students should recognize the issue of uncertainty inherent in many Artificial Intelligence applications, understand basic methods for dealing with this issue and learn to adopt and comprehend concrete algorithms that implement these methods. The focus in the first half of the semester will be on reasoning under uncertainty, whereas the second half will deal with learning.
Lehrmethodik
The course consists on a mix between theoretical lectures and practical exercises. Slides and teaching will be in English.
eLearning
Moodle
Inhalt/e
Provides an introduction to selected methods for dealing with uncertainty in Artificial Intelligence and Knowledge-Based Systems.
Topics
- Uncertainty in AI Systems
- Bayesian Inference and Bayesian Networks
- Machine Learning
Literatur
Adnan Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press. 2009 P.
Tan, M. Steinbach, V. Kumar. Introduction to Data Mining. Pearson. 2006
Stuart Russell and Peter Norvig: Artificial Intelligence: A modern approach. Prentice Hall, 2009
Judea Pearl: Probabilistic Reasoning in Intelligent Systems - Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc. 1988
D. Koller, N. Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. 2009
D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012
T. Mitchell. Machine Learning. McGraw Hill. 1997
Prüfungsinformationen
Prüfungsmethode/n
Written Exam at the end of the lectures + Minitests during the semester
Prüfungsinhalt/e
All the topics treated during the lectures.
Beurteilungskriterien/-maßstäbe
90% of the score will be given by the performance of the final exam. 10% comes from the performance achieved during the lectures, evaluated through mini-tests and participation.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 19W.2)
-
Fach: Vertiefung Informatik
(Wahlfach)
-
7.3 Einführung in die Artificial Intelligence II (
2.0h VC / 3.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 3.0 ECTS) Absolvierung im 4., 5., 6. Semester empfohlen
-
7.3 Einführung in die Artificial Intelligence II (
2.0h VC / 3.0 ECTS)
-
Fach: Vertiefung Informatik
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 17W.1)
-
Fach: Medieninformatik
(Wahlfach)
-
4.2 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
4.2 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Medieninformatik
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 17W.1)
-
Fach: Natural Language Processing
(Wahlfach)
-
5.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
5.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Natural Language Processing
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 17W.1)
-
Fach: Softwareentwicklung
(Wahlfach)
-
6.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
6.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Softwareentwicklung
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 17W.1)
-
Fach: Wirtschaftsinformatik
(Wahlfach)
-
7.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
7.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Wirtschaftsinformatik
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 12W.1)
-
Fach: Medieninformatik
(Wahlfach)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Fach: Medieninformatik
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 12W.1)
-
Fach: Natural Language Processing
(Wahlfach)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Fach: Natural Language Processing
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 12W.1)
-
Fach: Softwareentwicklung
(Wahlfach)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Fach: Softwareentwicklung
(Wahlfach)
- Bachelorstudium Angewandte Informatik
(SKZ: 511, Version: 12W.1)
-
Fach: Wirtschaftsinformatik
(Wahlfach)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Fach: Wirtschaftsinformatik
(Wahlfach)
- Masterstudium Angewandte Informatik
(SKZ: 911, Version: 13W.1)
-
Fach: Vertiefung Informatik
(Pflichtfach)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Fach: Vertiefung Informatik
(Pflichtfach)
- Bachelorstudium Wirtschaftsinformatik
(SKZ: 522, Version: 20W.2)
-
Fach: Spezialisierung Angewandte Informatik
(Wahlfach)
-
Spezialisierung Angewandte Informatik (
0.0h VO, VC, KS, UE / 6.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 3.0 ECTS) Absolvierung im 6. Semester empfohlen
-
Spezialisierung Angewandte Informatik (
0.0h VO, VC, KS, UE / 6.0 ECTS)
-
Fach: Spezialisierung Angewandte Informatik
(Wahlfach)
- Masterstudium Informationsmanagement
(SKZ: 922, Version: 13W.2)
-
Fach: Informatik
(Pflichtfach)
-
1.1 Knowledge Engineering für Informationsmanagement (
2.0h KS / 4.0 ECTS)
- 621.062 Introduction to Artificial Intelligence 2 (2.0h VC / 4.0 ECTS)
-
1.1 Knowledge Engineering für Informationsmanagement (
2.0h KS / 4.0 ECTS)
-
Fach: Informatik
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
- Sommersemester 2024
-
Sommersemester 2023
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)
-
Wintersemester 2022/23
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)
-
Sommersemester 2022
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)
-
Wintersemester 2021/22
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)
-
Sommersemester 2021
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)
-
Wintersemester 2020/21
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)
-
Sommersemester 2020
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)
-
Wintersemester 2019/20
- 621.062 VC Einführung in die Artificial Intelligence II (2.0h / 3.0ECTS)