621.066 (24S) Introduction to Artificial Intelligence 2 - Group B
Überblick
- Lehrende/r
- LV-Titel englisch Introduction to Artificial Intelligence 2 - Group B
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 28 (30 max.)
- Organisationseinheit
- Unterrichtssprache Deutsch
- LV-Beginn 05.03.2024
- 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 supervised and unsupervised machine learning.
Lehrmethodik
Lecture mixed with practical home and in-class exercises. Slides will be in English. Teaching language will be German unless there are non-German-speaking participants, otherwise English.
eLearning
Moodle
Inhalt/e
Provides an introduction to selected methods for machine learning and approaches for dealing with uncertainty in Artificial Intelligence.
Topics
- Uncertainty in AI Systems
- Bayesian Inference and Bayesian Networks
- Unsupervised Machine Learning
- Supervised Machine Learning
Literatur
Adnan Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press. 2009
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Pearson. 2006
Stuart Russell, 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
Daphne Koller, Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. 2009
David Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012
Tom Mitchell. Machine Learning. McGraw Hill. 1997
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning. Springer. 2021
Prüfungsinformationen
Prüfungsmethode/n
Written midterm and final exams, 4 practical classes with exercise sheets to be prepared.
Prüfungsinhalt/e
All the topics treated during the course.
Beurteilungskriterien/-maßstäbe
To pass this course, at least half of the sum of all exam points (over both exams) have to be reached, and half of all exercises contained in the exercise sheets have to be prepared.
Bonus points can be earned through the voluntary presentation of exercises and through active participation in class.
To determine your final grade, the following three components are added:
- your achieved sum of points over both exams (max 120)
- 0.8 times the percentage of your done exercises (max 80)
- your earned collaboration points
The resulting sum S along with the grading scheme below yields your final mark:
S >= 175 | grade 1 |
175 > S >= 150 | grade 2 |
150 > S >= 125 | grade 3 |
125 > S >= 100 | grade 4 |
100 > S | grade 5 |
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)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (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)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (2.0h VC / 3.0 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)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (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)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (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)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (2.0h VC / 4.0 ECTS)
-
7.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Wirtschaftsinformatik
(Wahlfach)
- Bachelorstudium Wirtschaftsinformatik
(SKZ: 522, Version: 20W.2)
-
Fach: Spezialisierung Angewandte Informatik
(Wahlfach)
-
Spezialisierung Angewandte Informatik (
0.0h VO, VC, KS, UE / 6.0 ECTS)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (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 Mathematics
(SKZ: 401, Version: 18W.1)
-
Fach: Informatics
(Wahlfach)
-
8.4 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (2.0h VC / 4.0 ECTS)
-
8.4 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Informatics
(Wahlfach)
- Bachelorstudium Robotics and Artificial Intelligence
(SKZ: 295, Version: 22W.1)
-
Fach: Artificial Intelligence
(Pflichtfach)
-
4.2 Introduction to Artificial Intelligence II (
2.0h VC / 3.0 ECTS)
- 621.066 Introduction to Artificial Intelligence 2 - Group B (2.0h VC / 3.0 ECTS)
-
4.2 Introduction to Artificial Intelligence II (
2.0h VC / 3.0 ECTS)
-
Fach: Artificial Intelligence
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Sommersemester 2024
- 621.064 VC Introduction to Artificial Intelligence 2 - Group A (2.0h / 3.0ECTS)
-
Wintersemester 2023/24
- 621.062 VC Introduction to Artificial Intelligence 2 (2.0h / 3.0ECTS)
-
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)