700.395 (17W) Data Mining and Neurocomputing
Überblick
- Lehrende/r
- LV-Titel englisch Data Mining and Neurocomputing
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 23 (25 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- mögliche Sprache/n der Leistungserbringung Deutsch , Englisch
- LV-Beginn 20.11.2017
- eLearning zum Moodle-Kurs
- Seniorstudium Liberale Ja
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
The students
- are able use the basic methods for datamining and neurocomputing (Deep Learning),
- have a good command of datamining approaches for different applications
- are able to transfer the acquired knowledge to solve complex machine learning applications for industry and research,
- are able to describe the state-of-the-art of the presented topics
Lehrmethodik inkl. Einsatz von eLearning-Tools
The course has two major parts. The first part consists of the theoretical and methodic fundamentals that will be introduced during the lecture. The second part consists of intensive lab work where students can implement, test, and apply the presented methods. The major lab language will be python using TensorFlow and Scikit-Learn libraries.
Inhalt/e
The course focuses on data selection, cleaning and preparation. Then, it covers the topics of dimension reduction, for example using principle components analysis and Auto-encoders. Additionally, different types of linear and non-linear models will be discussed in details with focus on neural models. Finally, different evaluation metrics and performance analysis s will be presented.
- Mathematical basics
- Data selection and preparation
- Bayesian models
- Linear models
- Non linear models
- Artificial Neural Networks
- Feedforward Neural Networks
- Backpropagation
- Recurrent Neural Networks
- Deep Learning
- Evaluation Metrics
Erwartete Vorkenntnisse
Basic knowledge of any programming language
Literatur
Data Mining: Practical Machine Learning Tools and Techniques# Publisher: Morgan Kaufmann; 2 edition (June 22, 2005)# Ian H. Witten, Eibe Frank# Language: English# ISBN-10: 0120884070# ISBN-13: 978-0120884070
Principles of Neuro Computing for Science & Engineering# Fredric M. Ham# Ivica Kostanic# ISBN: 0-07-025966-6# Publisher: McGraw-HillGeorge Papadourakis, “Introduction to Neural Networks”, Lecture Notes
Prüfungsinformationen
Prüfungsmethode/n
Written Exam + Project
Prüfungsinhalt/e
The student should pass a written exam successfully which reflects the understanding of the presented concepts and approaches. Additionally, the students should design and implement a fully working system in the field of machine learning.
Beurteilungskriterien/-maßstäbe
The final evaluation is divided as follows:
70% Exam
30% Project
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
- 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Autonomous Systems and Robotics (WI)
(Wahlfach)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
- 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Fach: Autonomous Systems and Robotics (WI)
(Wahlfach)
- Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)
-
Fach: Research Track (Methodischer Schwerpunkt)
(Pflichtfach)
-
4.2'-4.3' Theoretisch-Methodische Lehrveranstaltung I/II (
0.0h VO/VK/VS/KU/PS / 6.0 ECTS)
- 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
-
4.2'-4.3' Theoretisch-Methodische Lehrveranstaltung I/II (
0.0h VO/VK/VS/KU/PS / 6.0 ECTS)
-
Fach: Research Track (Methodischer Schwerpunkt)
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
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Wintersemester 2023/24
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Wintersemester 2022/23
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Wintersemester 2021/22
- 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
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Wintersemester 2020/21
- 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
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Sommersemester 2020
- 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
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Sommersemester 2019
- 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
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Wintersemester 2016/17
- 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
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Wintersemester 2015/16
- 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
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Wintersemester 2013/14
- 700.395 VK Data Mining in Intelligent Transportation and Logistics (2.0h / 4.0ECTS)
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Wintersemester 2012/13
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