623.504 (24S) Artificial Intelligence & Machine Learning
Überblick
- Lehrende/r
- LV-Titel englisch Artificial Intelligence & Machine Learning
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 28 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 05.03.2024
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The course provides a practical introduction into artificial intelligence methods with a focus on machine learning and it’s applications in computer science.
Please consider also visiting:
- VK "Machine Learning and Deep Learning" (650.025) for an in-depth overview of neural networks and their applications.
- PR "Machine Learning and Deep Learning" (623.625) providing hands-on sessions with a focus on programming of applications using deep neural networks
- "Selected Topics in Artificial Intelligence" (626.017) for an in-depth review of reinforcement learning methods.
- ”Current Topics in Multimedia Systems: Content Search with Deep Learning” (623.915) which among other interesting topics considers various architectures and applications of Deep Neural Networks to image/video processing and recognition.
Lehrmethodik
Lectures with a student's project applying machine learning to a practical problem.
Inhalt/e
- Introduction to AI and machine learning
- Supervised learning: classification and regression
- Unsupervised learning: transformation of data and clustering
- Validation of models
- Overview of the reinforcement learning
Erwartete Vorkenntnisse
The course has makes no assumptions about the prior knowledge, but basic knowledge of the probability theory as well as of Python is a plus.
Curriculare Anmeldevoraussetzungen
No prerequisites
Literatur
Beginners:
- James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer
- Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.
Classics:
- Mitchell, T. (1997) Machine Learning. McGraw Hill.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
- Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning. 2nd edition, Springer.
Prüfungsinformationen
Prüfungsmethode/n
Design and implementation of a project applying machine learning to solve a selected problem
Prüfungsinhalt/e
Students should demonstrate their ability to apply machine learning methods discussed in the course.
Beurteilungskriterien/-maßstäbe
Grades are given based on a project proposal, a mid-term review presentation, and a final report.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Informatics
(SKZ: 911, Version: 19W.2)
-
Fach: Vertiefung Informatik (Specialization in Informatics)
(Pflichtfach)
-
1.4 Artificial Intelligence & Machine Learning (
2.0h VC / 4.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 2. Semester empfohlen
-
1.4 Artificial Intelligence & Machine Learning (
2.0h VC / 4.0 ECTS)
-
Fach: Vertiefung Informatik (Specialization in Informatics)
(Pflichtfach)
- Masterstudium Artificial Intelligence and Cybersecurity
(SKZ: 993, Version: 20W.1)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 2., 3. Semester empfohlen
-
Fachlich relevante Lehrveranstaltungen (
0.0h XX / 34.0 ECTS)
-
Fach: Specialisation in Artificial Intelligence and Cybersecurity
(Wahlfach)
- Masterstudium Information Management
(SKZ: 922, Version: 19W.1)
-
Fach: Information and IT Management
(Pflichtfach)
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
-
Fach: Information and IT Management
(Pflichtfach)
- Masterstudium Information Management
(SKZ: 922, Version: 19W.1)
-
Fach: Specialisation in Information Management
(Wahlfach)
-
Specialisation in Information Management (
0.0h VO, VC, KS / 16.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
Specialisation in Information Management (
0.0h VO, VC, KS / 16.0 ECTS)
-
Fach: Specialisation in Information Management
(Wahlfach)
- Masterstudium Information Management
(SKZ: 922, Version: 23W.1)
-
Fach: Information and IT Management
(Pflichtfach)
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
3.3 Current Topics in Information and IT Management (
0.0h VC, KS, SE / 4.0 ECTS)
-
Fach: Information and IT Management
(Pflichtfach)
- Masterstudium Information Management
(SKZ: 922, Version: 23W.1)
-
Fach: Specialisation in Information Management
(Wahlfach)
-
Specialisation in Information Management (
0.0h VO, VC, KS / 16.0 ECTS)
- 623.504 Artificial Intelligence & Machine Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
Specialisation in Information Management (
0.0h VO, VC, KS / 16.0 ECTS)
-
Fach: Specialisation in Information Management
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
-
Sommersemester 2024
- 623.505 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Wintersemester 2023/24
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2023
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Wintersemester 2022/23
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2022
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Wintersemester 2021/22
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2021
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2020
- 623.504 VC Artificial Intelligence & Machine Learning (2.0h / 4.0ECTS)