621.064 (24S) Introduction to Artificial Intelligence 2 - Group A
Überblick
- Lehrende/r
- LV-Titel englisch Introduction to Artificial Intelligence 2 - Group A
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 34 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 04.03.2024
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Students should understand the different types of algorithms, comprehending the intrinsic differences and having an introductory view on all aspects of AI. 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 methods. In this course, there will be a particular focus on neural networks, including fully connected networks, CNN and Transformers.
Lehrmethodik
The course consists on a mix between theoretical lectures and practical exercises. After every lecture on a topic, you will have an exercise sheet assigned to do at home, and a small minitest (15 minutes) will take place during the next lecture. The will be no programming exercises during this course. Lectures will be in presence, with no online option unless specified otherwise. The presence is not compulsory, but the minitests will be in presence (no online option), therefore you should be present at least in the days when minitests are held. 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
- "Classic" Machine Learning
- Neural Networks
- CNN
- Transformers
- Clustering algorithms
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
Josh Starmer, The StatQuest Illustrated Guide To MachineLearning, 1stEdition. 2022.
Prüfungsinformationen
Prüfungsmethode/n
Written Exam at the end of the lectures + Minitests during the semester. There will be 6 minitests during the semester. 10 points per minitest. At the end of the semester, you must have collected at least 30 points to qualify for the exam. Points can be earned also through exercise demonstration and active participation in class
Prüfungsinhalt/e
All the topics treated during the lectures.
Beurteilungskriterien/-maßstäbe
75% of the score will be given by the performance of the final exam. 25% 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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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)
- 621.064 Introduction to Artificial Intelligence 2 - Group A (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
- 621.066 VC Introduction to Artificial Intelligence 2 - Group B (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)