700.395 (24W) Data Mining, Synthetic Data, and Knowledge Discovery
Überblick
- Lehrende/r
- LV-Titel englisch Data Mining, Synthetic Data, and Knowledge Discovery
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Blended-Learning-Lehrveranstaltung
- Online-Anteil 77%
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 0 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 14.10.2024
- Seniorstudium Liberale Ja
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Welcome to an exciting journey into Data Mining, Synthetic Data, and Knowledge Discovery. This comprehensive course will provide you with a deep understanding of the fundamental approaches and techniques employed in these domains. Our primary objective is to equip you with the essential knowledge and skills necessary to tackle a wide range of supervised, unsupervised, and synthetic data generation challenges across various industries and research domains. You will master the art of extracting valuable insights, patterns, and knowledge from large and complex datasets, empowering you to make informed decisions and accurate predictions. In addition, you will deeply explore the realm of synthetic data generation, particularly harnessing the power of architectures like Autoencoders, Generative Adversarial Networks (GANs) and diffusion models. Throughout the course, you will have the unique opportunity to explore real-world use cases and delve into the latest applications from cutting-edge research. By immersing yourself in practical scenarios, you will gain a firsthand understanding of how these techniques are applied, enhancing your appreciation for their significance in today's data-driven world.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The teaching methodology for this course on Data Mining, Synthetic Data, and Knowledge Discovery integrates several instructional strategies to ensure comprehensive understanding and real-world application of concepts. Regular online tutorials will provide students with practical examples and exercises to reinforce lecture content, clarify doubts, and encourage questions. Students will undertake practical projects, applying acquired knowledge and skills to real-world problems, working with complex datasets, extracting insights, patterns, and knowledge, and generating synthetic data using architectures like Autoencoders, GANs, and diffusion models. The eLearning platform will host a variety of online resources, including research papers, articles, and tutorials, offering additional insights into the latest field advancements. In-class quizzes and assessments will evaluate students' understanding and practical application of concepts, with feedback provided for improvement.
Inhalt/e
Data Mining and Knowledge Discovery
- Data preprocessing
- Supervised Learning (Support Vector Machine (SVMs), Bayes Classifiers, Decision Trees)
- Unsupervised Learning and Clustering (K-means, hierarchical clustering)
- Dimensionality Reduction (Singular Value Decomposition (SVD), Principal Component Analysis (PCA)
- Regularization Techniques
- Kernel Models
- Evaluation Metrics
Synthetic Data
- Introduction to Neurocomputing (Activation Functions, Backpropagation)
- Autoencoders
- Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Diffusion models
Erwartete Vorkenntnisse
This course's prerequisites typically include basic programming skills, preferably in Python, as many tasks will involve coding. A solid mathematical background in linear algebra, calculus, and statistics is crucial for understanding and implementing data mining techniques.
Curriculare Anmeldevoraussetzungen
No requirements.
Literatur
- Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, and Jian Pei: This book provides a comprehensive introduction to the concepts and techniques of data mining.
- Pattern Recognition and Machine Learning by Christopher Bishop: Although not exclusively focused on data mining, this book provides a comprehensive introduction to pattern recognition and machine learning, which are essential concepts for data mining and knowledge discovery.
- Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster: This book provides a comprehensive introduction to generative modeling using deep learning architectures like Autoencoders, GANs, and diffusion models.
Prüfungsinformationen
Prüfungsmethode/n
Format
- An oral exam using a digital platform (e.g., BBB via Moodle).
Scheduling
- Students will be given a specific time slot at least one week before the exam date.
- It is crucial for students to check their technical setup before the exam day to avoid any technical difficulties.
Prüfungsinhalt/e
Preparation Tips
- Review course materials, including lecture notes, readings, and assignments.
- Practice speaking and explaining concepts to peers or in front of a mirror.
- Think critically about the topics and anticipate potential counterarguments or challenges.
Beurteilungskriterien/-maßstäbe
Evaluation Components
- Project: 20%
- Homework: 20%
- Class Activity: 10%
- Online Oral Exam: 50%
Evaluation Criteria
- Depth of Understanding: Demonstrated thorough comprehension of course content
- Ability to connect course concepts to real-world applications or personal experiences.
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, Synthetic Data, and Knowledge Discovery (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, Synthetic Data, and Knowledge Discovery (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, Synthetic Data, and Knowledge Discovery (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, Synthetic Data, and Knowledge Discovery (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, Synthetic Data, and Knowledge Discovery (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, Synthetic Data, and Knowledge Discovery (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 and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: Autonomous Systems and Robotics: Advanced
(Wahlfach)
-
2.2 Data Mining and Neurocomputing (
0.0h VC / 4.0 ECTS)
- 700.395 Data Mining, Synthetic Data, and Knowledge Discovery (2.0h VC / 4.0 ECTS)
-
2.2 Data Mining and Neurocomputing (
0.0h VC / 4.0 ECTS)
-
Fach: Autonomous Systems and Robotics: Advanced
(Wahlfach)
- Masterstudium Mathematics
(SKZ: 401, Version: 18W.1)
-
Fach: Information and Communications Engineering
(Wahlfach)
-
9.5 Data Mining and Neurocomputing (
2.0h VC / 4.0 ECTS)
- 700.395 Data Mining, Synthetic Data, and Knowledge Discovery (2.0h VC / 4.0 ECTS)
-
9.5 Data Mining and Neurocomputing (
2.0h VC / 4.0 ECTS)
-
Fach: Information and Communications Engineering
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
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Wintersemester 2023/24
- 700.395 VC Data Mining, Synthetic Data, and Knowledge Discovery (2.0h / 4.0ECTS)
-
Wintersemester 2022/23
- 700.395 VC Data Mining, Synthetic Data and Knowledge Discovery (2.0h / 4.0ECTS)
-
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)
-
Sommersemester 2019
- 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
-
Wintersemester 2017/18
- 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)
-
Wintersemester 2013/14
- 700.395 VK Data Mining in Intelligent Transportation and Logistics (2.0h / 4.0ECTS)
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Wintersemester 2012/13
- 700.395 VK Data Mining in Intelligent Transportation and Logistics (2.0h / 4.0ECTS)