Module im Bereich: Fundamentals of Data Science

⚠️ Hinweise zu Modulen im Bereich: Fundamentals of Data Science
Im Folgenden werden Module für den Bereich Fundamentals of Data Science aus anderen Fakultäten aufgelistet. Wir verweisen auf die Modulbeschreibungen der jeweiligen anbietenden Fakultät; sie können in den Modulhandbüchern der entsprechenden Fakultäten gefunden werden.
Abweichende Module müssen vom Studiengangsleitenden genehmigt werden.
Mathematik
Statistical Methods (Statistical Methods II) (Winter)

Data Mining I - Introduction to Data Mining
Summer
Introduction to Data Mining
(in German: Data Mining I - Introduction to Data Mining
)
Module-ID: FIN-INF-120454
Abbreviation
DM1_ENG
Credit Points
6
Semester
Summer
Term
starting at 1.
Duration
1 Semester
Language
english
Level
Master
Link:
 LSF
Limited accessability:
false
Responsibility:
Myra Spiliopoulou
Lecturer:
Myra Spiliopoulou
Classes:
Vorlesung DM1_ENG
Übung DM1_ENG
COURTESY TRANSLATION:
Lecture class DM1_ENG
Exercise class DM1_ENG
 
Applicability in curriculum:
- M.Sc. DKE: Fundamentals of Data Science
- M.Sc. DE: Methoden der Informatik
- M.Sc. VC: Computer Science
Intended learning outcomes:
When successfully completing this course, the students
comprehend the purpose of model induction from data
are familiar with the behaviour of simple learning algorithms on tabular static data and can apply them to derive models
comprehend the purpose, challenges and basic instruments for model evaluation
can evaluate models
comprehend the purpose of data engineering in preparation of data mining
are familiar with basic tasks of data engineering
and have thus acquired skills they need in order to design themselves learning algorithms, and to build workflows for model evaluation.
Content:
Underpinnings: basic forms of learning
BLOCK 1 - supervised: Classification algorithms and evaluation procedured
BLOCK 2 - unsupervised: Clustering algorithms and evaluation procedures
BLOCK Evaluation: more on model evaluation and model comparison
BLOCK Data Engineering: basic steps on engineering the data before they are input to the model learner
Workload:
28 hours - in presence : lecture class (2 hours per week)
28 hourse in presence: exercise class (2 hours per week)
124 hours: independent work (for lectures, exerices and for the exam preparation)
Pre-examination requirements:
Type of examination:
Teaching method / lecture hours per week (SWS):
A minimum number of points must be achieved during the course.
Schriftliche Prüfung - Klausur (120 min)
COURTESY TRANSLATION:
Written exam of the form 'Klausur' (120 min)
Vorlesung (2 SWS)
Übung (2 SWS)
COURTESY TRANSLATION:
Lecture (2 hours per week of the semester)
Exercise (2 hours per week of the semester)
Prerequisites according to examination regulations:
Recommended prerequisites:
keine
Databases
Data management
Basics in statistics
Media:
Literature:
Book M: The book of the course is Introduction to Data Mining by Pan-Ning Tan, Michael Steinbach, Anuj Karpatne and Vipin Kumar. PEARSON, 2019 (2nd edition)
Chapters 2 and 3
Parts of Chapters 4, 6, 7
Parts of Chapters 8 and 10
Overview of the book under https://www-users.cs.umn.edu/~kumar001/dmbook/index.php
including slide collections etc
To read the full textbook (electronic version): Find the ebook from the library here, follow the direct link, and log in -> Shibboleth login -> Otto-von-Guericke University -> Log in -> read.
Note: Some slides come from the 1st edition. They concern content that did not change between the two editions.
Book D: Data Preprocessing in Data Mining by Salvador Garcia, Julian Luengo and Fransisco Herrera, SPRINGER International Publishing Switzerland, 2015****
This book is used in the Block 'Data Engineering' (Block 'Data' for short). This book is available as pdfs from our library. The chapters of relevance are mentioned in the description of the course block.****
Comments:
This module is intended for students in low semesters of 4-semester-long master degrees and assumes a limited background in data science. It is not appropriate for students with a bachelor degree in data science.

Data and Knowledge Engineering
Sommer
(in German: Data and Knowledge Engineering
)
Module-ID: FIN-INF-120500
Abbreviation
Credit Points
6
Semester
Sommer
Term
starting at 1.
Duration
1 Semester
Language
english
Level
Master
Link:
 LSF
Limited accessability:
Responsibility:
Andreas Nürnberger
Lecturer:
Andreas Nürnberger
Classes:
Seminar Data and Knowledge Engineering
 
Applicability in curriculum:
- M.Sc. INF: Schlüssel- und Methodenkompetenzen
- M.Sc. INGINF: Schlüssel- und Methodenkompetenzen
- M.Sc. WIF: Schlüssel- und Methodenkompetenzen
- M.Sc. DKE: Fundamentals of Data Science
- M.Sc. VC: Schlüssel- und Methodenkompetenzen
Intended learning outcomes:
Students who complete the course ...
understand how to work in practice with DKE approaches
can perform a literature review on the state-of-the-art on a given topic
can describe a given DKE task in form of a scientific paper
can present and defend a practical project by a presentation and discussion
Content:
The seminar focuses on applying machine learning techniques to a hands-on problem, resulting in a paper and short presentation in one of the following research areas:
Information Retrieval:
Document organization
Information management
Ranking & Explainability
Natural Language Processing
Named Entity Recognition
Tokenization & Filtering
Latent Representations of Words / Sentences / Documents
Human Computer Interaction
Usability
User Experience (UX) Design
Human-AI interaction
Workload:
180 h self study
Pre-examination requirements:
Type of examination:
Teaching method / lecture hours per week (SWS):
Written report and oral presentation (Referat). Requirements for exam participation will be announced in the first week of the course in class and online.
Seminar
Prerequisites according to examination regulations:
Recommended prerequisites:
keine
Media:
Literature:
Comments:

Introduction to Simulation
Winter
(in German: Introduction to Simulation
)
Module-ID: FIN-INF-120345
Abbreviation
ItS
Credit Points
6
Semester
Winter
Term
starting at 1.
Duration
1 Semester
Language
english
Level
Master
Link:
 LSF
Limited accessability:
Responsibility:
Graham Horton
Lecturer:
Graham Horton
Classes:
Lecture Introduction to Simulation
Exercise class Introduction to Simulation
 
Applicability in curriculum:
- M.Sc. DKE: Fundamentals of Data Science
- M.Sc. DE: Grundlagen Informatik
- M.Sc. DE: Informatik Grundlagen (SPO 2027)
Intended learning outcomes:
Students who complete the course ...
can analyse discrete, continuous and hybrid systems using a professional simulation tool
know how to obtain an accurate result efficiently when integrating initial value problems numerically
can carry out input and output analysis to obtain statistically significant results for stochastic, discrete-event models
can select an appropriate modelling paradigm for a given situation
can perform a simulation study and interpret its results
Content:
Introduction to the fundamentals and steps in a simulation study:
discrete-event simulation
random variables and random number generation
statistical data analysis
ordinary differential equations and numerical integration
stochastic Petri nets
the AnyLogic simulation system
discrete-time Markov chains
agent-based simulation
Workload:
56 contact hours + 124 h self study
Pre-examination requirements:
Type of examination:
Teaching method / lecture hours per week (SWS):
2h written exam
2 SWS lecture
2 SWS exercise class
Prerequisites according to examination regulations:
Recommended prerequisites:
keine
Basic engineering mathematics
Media:
Literature:
Banks, Carson, Nelson, Nicol: Discrete-Event System Simulation
Comments:

Logik 2
Winter
 
(engl. Logic 2
)
Modulnummer: FIN-INF-0
Kürzel
Logik2
CP
10
Semester
Winter
Fachsem.
ab 4.
Dauer
1 Semester
Sprache
deutsch
Niveau
Bachelor,Master
Link zum LSF:
 LSF
Zugang beschränkt:
true
Verantwortung:
Fabian Neuhaus
Dozent:in:
Fabian Neuhaus
Lehrveranstaltungen:
Vorlesung Logik 2
Übung Logik 2
Verwendbarkeit:
- B.Sc. INF: Informatik - Wahlpflicht
- B.Sc. INF: Informatik Wahlpflicht (SPO 2027)
- B.Sc. CV: Informatik - Wahlpflicht
- B.Sc. CV: Computervisualistik Wahlpflicht (SPO 2027)
- B.Sc. INGINF: Informatik - Wahlpflicht
- B.Sc. INGINF: Informatik Wahlpflicht (SPO 2027)
- B.Sc. WIF: Verstehen und Gestalten - Wahlpflicht
- B.Sc. WIF: Informatik Wahlpflicht (SPO 2027)
- B.Sc. INF (bilingual): Informatik - Wahlpflicht
- B.Sc. INF (bilingual): Informatik Wahlpflicht (SPO 2027)
- M.Sc. INF: Informatik
- M.Sc. INF: Informatik Wahlpflicht (SPO 2027)
- M.Sc. INGINF: Informatik
- M.Sc. WIF: Informatik
- M.Sc. WIF: Informatik Wahlpflicht (SPO 2027)
- M.Sc. DKE: Fundamentals of Data Science
- M.Sc. DE: Grundlagen Informatik
- M.Sc. DE: Informatik Grundlagen (SPO 2027)
- M.Sc. VC: Computer Science
- M.Sc. VC: Computer Science (SPO 2027)
Angestrebte Lernergebnisse:
Anwendungen von ausgewählten Erweiterungen der klassischen Logik zur Wissensrepräsentation
Anwendungen von ausgewählten nicht-klassischen Logiken zur Wissensrepräsentation
Anwendung von Beweiskalkülen für  Logiken
Verstehen von fortgeschrittenen semantischen Methoden für Logiken
Verstehen von metalogischen Beweisen und die Fähigkeit einfache metalogische Beweise selbst zu führen
Inhalt:
Die Veranstaltung baut auf der Pflichtveranstaltung Logik auf, in der die klassische Aussagen- und Prädikatenlogik eingeführt wird.  In Logik 2 werden einerseits weiterführende Themen der klassischen Logik behandelt (u.a.  Korrektheits-, Vollständigkeits- und Kompaktheitsbeweise).  Darüber hinaus werden Erweiterungen der klassischen Logik sowie nichtklassische Logiken behandelt, z.B. Modallogik, Temporallogik, epistemische Logik, Konditionallogik, mehrwertige Logiken und Logik höherer Stufe.
Arbeitsaufwand:
Präsenzzeiten: 14 X 4h = 56h
selbständige Arbeit = 244h
(Vorbereiten der Vorlesung,
Lösen Übungsaufgaben,
Vorbereitung Präsentation)
Prüfungsvorleistungen:
Studien-/Prüfungsleistungen:
Lehrform / SWS:
2 Drittel der Übungsaufgaben votiert
BA: mindestens 1 Präsentation
MA: mindestens 2 Präsentationen
Mündliche Prüfung
Übung 2 SWS
Vorlesung 2 SWS
Voraussetzungen nach Prüfungsordnung:
Empfohlene Voraussetzungen:
keine
Erfolgreicher Abschluss des Modul Logik bzw. fundiertes Wissen im Bereich der Themen des Moduls"Logik.
Medienformen:
Literatur:
Hinweise:
Anmeldung via E-Learning-Plattform

Machine Learning
Winter
(in German: Machine Learning
)
Module-ID: FIN-INF-120517
Abbreviation
ML
Credit Points
6
Semester
Winter
Term
starting at 1.
Duration
1 Semester
Language
english
Level
Master
Link:
 LSF
Limited accessability:
Responsibility:
Andreas Nürnberger
Lecturer:
Andreas Nürnberger
Classes:
Lecture Machine Learning
Exercise class Machine Learning
 
Applicability in curriculum:
- M.Sc. INF: Informatik
- M.Sc. INGINF: Informatik
- M.Sc. WIF: Informatik
- M.Sc. DKE: Fundamentals of Data Science
- M.Sc. DE: Methoden der Informatik
- M.Sc. VC: Computer Science
Intended learning outcomes:
Students ...
can create machine learning pipelines and individual algorithms
program decision trees, multi-layer perceptrons, and KNN classifiers in Java, Python, C++
can evaluate the predictive performance of various classifiers for practical scenarios
Content:
Introduction to concept spaces and concept learning
Algorithms for instance-based learning and  cluster analysis
Algorithms for generating  decision trees
Bayesian learning
Neural networks
Association learning
Reinforcement learning
Hypothesis evaluation
Workload:
56 contact hours + 124h self study
Pre-examination requirements:
Type of examination:
Teaching method / lecture hours per week (SWS):
Written exam (also for pass/fail grade).  Requirements for exam participation will be announced in the first week of the course in class and online.
Lecture (2 SWS)
Exercise class (2 SWS)
Prerequisites according to examination regulations:
Recommended prerequisites:
keine
Algorithmen und Datenstrukturen
Media:
Literature:
Comments:
Can only be credited if the module "Grundlagen des Maschinellen Lernens" has not been credited already.

Principles and Practices of Scientific Work and Soft Skills
Summer
(in German: Principles and Practices of Scientific Work and Soft Skills
)
Module-ID: FIN-INF-120502
Abbreviation
PPSW
Credit Points
6
Semester
Summer
Term
starting at 1.
Duration
1 Semester
Language
english
Level
Master
Link:
 LSF
Limited accessability:
true
Responsibility:
PD Dr. Claudia Krull
Lecturer:
PD Dr. Claudia Krull
Classes:
Lecture PPSW
Exercise PPSW
 
Applicability in curriculum:
- M.Sc. INF: Schlüssel- und Methodenkompetenz (SPO 2027)
- M.Sc. WIF: Schlüssel- und Methodenkompetenz (SPO 2027)
- M.Sc. DKE: Fundamentals of Data Science
- M.Sc. DE: Human Factors
- M.Sc. DE: Schlüssel- und Methodenkompetenz (SPO 2027)
- M.Sc. VC: Schlüssel- und Methodenkompetenz (SPO 2027)
Intended learning outcomes:
Students know how a Goal is defined and are able to formulate it properly. based on that students know methods for time management.
Students understand important aspects of project management and teamwork, including phases and pitfalls.
Students are able to conduct a systematic literature review in order to gain an overview of an area of interest.
Students are able to identify and formulate research hypothesis
Students understand how a scientific publication is structured, can read it and extract the important information.
Students are able to write a research proposal on a selected topic in the form of a scientific publication.
Students understand how to present a research topic proposal on a selected topic.
Content:
The course covers the following topics, among others:
Introduction to Scientific Work
Literature Research and Management Research Projects and Thesis Topics
Scientific Writing - Thesis Structure and Writing Techniques
Study Skills & Self Management
Project Management & Team Work
Presentation Skills
The project and term paper topic can be related to an ongoing research project or be used for Master’s thesis preparation.
Workload:
40 h in person teaching
140 h independent work
Pre-examination requirements:
Type of examination:
Teaching method / lecture hours per week (SWS):
participation in all classes
completion of assignments
keeping all deadlines
Report
Lecture (2 SWS)
Exercise (2 SWS)
Prerequisites according to examination regulations:
Recommended prerequisites:
keine
Media:
Literature:
Comments:
participation is limited to 40