# Module im Bereich: Fundamentals of Data Science

# ⚠️ Hinweise zu Modulen im Bereich: Fundamentals of Data Science

<span style="color: rgb(61, 61, 61);">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](https://www.verwaltungshandbuch.ovgu.de/Modulhandb%C3%BCcher-media_id-2398.html) gefunden werden.</span>

<span style="color: rgb(230, 126, 35);">**Abweichende Module müssen vom [Studiengangsleitenden](https://www.fin.ovgu.de/Studium/Vor+dem+Studium/Studiengangsleiter.html) genehmigt werden.**</span>

#### <span style="color: rgb(61, 61, 61);">Mathematik</span>

- <span style="color: rgb(61, 61, 61);">[Statistical Methods (Statistical Methods II)](https://lsf.ovgu.de/qislsf/rds?state=verpublish&status=init&vmfile=no&publishid=213908&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung) (Winter)</span>

# Data Mining I - Introduction to Data Mining

##### Summer 

#### Introduction to Data Mining 

##### (in German: Data Mining I - Introduction to Data Mining )

<table bgcolor="#7FB3D9" border="1" id="bkmrk-%5Btitle%5D" style="width: 100%; border-collapse: collapse; border-width: 1px; border-style: none;"><colgroup><col style="width: 100%;"></col></colgroup><tbody><tr style="background-color: rgb(235, 243, 249);"><td>##### <span style="color: rgb(0, 104, 180);">**Module-ID:** </span><span style="color: rgb(0, 104, 180);">**FIN-INF-120454** </span>

</td></tr></tbody></table>

<table bgcolor="#7FB3D9" border="1" id="bkmrk-" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 13.061%;"></col><col style="width: 15.442%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col></colgroup><tbody><tr><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Abbreviation**</span>

<span style="color: rgb(61, 61, 61);">DM1\_ENG </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Credit Points**</span>

<span style="color: rgb(61, 61, 61);">6 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Semester**</span>

<span style="color: rgb(61, 61, 61);">Summer </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Term**</span>

<span style="color: #3d3d3d;">starting at 1. </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);">**<span style="color: rgb(61, 61, 61);">Duration</span>**

<span style="color: rgb(61, 61, 61);">1 Semester </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Language**</span>

<span style="color: rgb(61, 61, 61);">english</span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Level**</span>

<span style="color: rgb(61, 61, 61);">Master </span>

</td></tr></tbody></table>

<table border="1" id="bkmrk-verantwortung%3A-verwe" style="border-collapse: collapse; width: 100%; height: 124.8px; border-width: 1px; border-style: none;"><colgroup><col style="width: 24.7879%;"></col><col style="width: 75.2121%;"></col></colgroup><tbody><tr style="height: 35.4px;"><td style="height: 35.4px;">**Link:**</td><td style="height: 35.4px;">[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20261&veranstaltung.dtxt=Data+Mining+I+-+Introduction+to+Data+Mining&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Data Mining I - Introduction to Data Mining")</td></tr><tr><td>**Limited accessability:**</td><td>false </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Responsibility:**</td><td style="height: 29.8px;">Myra Spiliopoulou </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Lecturer:**</td><td style="height: 29.8px;">Myra Spiliopoulou </td></tr><tr><td>**Classes:**</td><td>- Vorlesung DM1\_ENG
- Übung DM1\_ENG

**COURTESY TRANSLATION:**- Lecture class DM1\_ENG
- Exercise class DM1\_ENG

 </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Applicability in curriculum:**</td><td style="height: 29.8px;">- M.Sc. DKE: Fundamentals of Data Science  
- M.Sc. DE: Methoden der Informatik  
- M.Sc. VC: Computer Science  
  
</td></tr></tbody></table>

**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)

  
<table border="1" id="bkmrk-studien-%2Fpr%C3%BCfungslei" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 32.0641%;"></col><col style="width: 34.9204%;"></col><col style="width: 33.0155%;"></col></colgroup><tbody><tr><td>**Pre-examination requirements:**</td><td>**Type of examination:**</td><td>**Teaching method / lecture hours per week (SWS):**</td></tr><tr><td>A minimum number of points must be achieved during the course.

</td><td>- Schriftliche Prüfung - Klausur (120 min)

**COURTESY TRANSLATION:**- Written exam of the form 'Klausur' (120 min)

</td><td>- Vorlesung (2 SWS)
- Übung (2 SWS)

**COURTESY TRANSLATION:**- Lecture (2 hours per week of the semester)
- Exercise (2 hours per week of the semester)

</td></tr></tbody></table>

<table border="1" id="bkmrk-voraussetzungen-nach" style="font-family: var(--font-body); font-size: 14px; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Prerequisites according to examination regulations:**</td><td>**Recommended prerequisites:**</td></tr><tr><td>keine

</td><td>- Databases
- Data management
- Basics in statistics

</td></tr></tbody></table>

<table border="1" id="bkmrk-medienformen%3A-litera" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Media:**</td><td>**Literature:**</td></tr><tr><td></td><td>**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](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 -&gt; Shibboleth login -&gt; Otto-von-Guericke University -&gt; Log in -&gt; 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.\*\*\*\*   
</td></tr></tbody></table>

**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 )

<table bgcolor="#7FB3D9" border="1" id="bkmrk-%5Btitle%5D" style="width: 100%; border-collapse: collapse; border-width: 1px; border-style: none;"><colgroup><col style="width: 100%;"></col></colgroup><tbody><tr style="background-color: rgb(235, 243, 249);"><td>##### <span style="color: rgb(0, 104, 180);">**Module-ID:** </span><span style="color: rgb(0, 104, 180);">**FIN-INF-120500** </span>

</td></tr></tbody></table>

<table bgcolor="#7FB3D9" border="1" id="bkmrk-" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 13.061%;"></col><col style="width: 15.442%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col></colgroup><tbody><tr><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Abbreviation**</span>

<span style="color: rgb(61, 61, 61);"></span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Credit Points**</span>

<span style="color: rgb(61, 61, 61);">6 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Semester**</span>

<span style="color: rgb(61, 61, 61);">Sommer </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Term**</span>

<span style="color: #3d3d3d;">starting at 1. </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);">**<span style="color: rgb(61, 61, 61);">Duration</span>**

<span style="color: rgb(61, 61, 61);">1 Semester </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Language**</span>

<span style="color: rgb(61, 61, 61);">english</span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Level**</span>

<span style="color: rgb(61, 61, 61);">Master </span>

</td></tr></tbody></table>

<table border="1" id="bkmrk-verantwortung%3A-verwe" style="border-collapse: collapse; width: 100%; height: 124.8px; border-width: 1px; border-style: none;"><colgroup><col style="width: 24.7879%;"></col><col style="width: 75.2121%;"></col></colgroup><tbody><tr style="height: 35.4px;"><td style="height: 35.4px;">**Link:**</td><td style="height: 35.4px;">[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20261&veranstaltung.dtxt=Data+and+Knowledge+Engineering&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Data and Knowledge Engineering")</td></tr><tr><td>**Limited accessability:**</td><td></td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Responsibility:**</td><td style="height: 29.8px;">Andreas Nürnberger </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Lecturer:**</td><td style="height: 29.8px;">Andreas Nürnberger </td></tr><tr><td>**Classes:**</td><td>Seminar *Data and Knowledge Engineering* </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Applicability in curriculum:**</td><td style="height: 29.8px;">- 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  
  
</td></tr></tbody></table>

**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 &amp; Explainability

Natural Language Processing - Named Entity Recognition
- Tokenization &amp; Filtering
- Latent Representations of Words / Sentences / Documents

Human Computer Interaction - Usability
- User Experience (UX) Design
- Human-AI interaction

**Workload:** 180 h self study

<table border="1" id="bkmrk-studien-%2Fpr%C3%BCfungslei" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 32.0641%;"></col><col style="width: 34.9204%;"></col><col style="width: 33.0155%;"></col></colgroup><tbody><tr><td>**Pre-examination requirements:**</td><td>**Type of examination:**</td><td>**Teaching method / lecture hours per week (SWS):**</td></tr><tr><td></td><td>Written report and oral presentation (*Referat*). Requirements for exam participation will be announced in the first week of the course in class and online.

</td><td>Seminar

</td></tr></tbody></table>

<table border="1" id="bkmrk-voraussetzungen-nach" style="font-family: var(--font-body); font-size: 14px; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Prerequisites according to examination regulations:**</td><td>**Recommended prerequisites:**</td></tr><tr><td>keine

</td><td></td></tr></tbody></table>

<table border="1" id="bkmrk-medienformen%3A-litera" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Media:**</td><td>**Literature:**</td></tr><tr><td></td><td></td></tr></tbody></table>

**Comments:**

# Introduction to Simulation

##### Winter 


##### (in German: Introduction to Simulation )

<table bgcolor="#7FB3D9" border="1" id="bkmrk-%5Btitle%5D" style="width: 100%; border-collapse: collapse; border-width: 1px; border-style: none;"><colgroup><col style="width: 100%;"></col></colgroup><tbody><tr style="background-color: rgb(235, 243, 249);"><td>##### <span style="color: rgb(0, 104, 180);">**Module-ID:** </span><span style="color: rgb(0, 104, 180);">**FIN-INF-120345** </span>

</td></tr></tbody></table>

<table bgcolor="#7FB3D9" border="1" id="bkmrk-" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 13.061%;"></col><col style="width: 15.442%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col></colgroup><tbody><tr><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Abbreviation**</span>

<span style="color: rgb(61, 61, 61);">ItS </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Credit Points**</span>

<span style="color: rgb(61, 61, 61);">6 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Semester**</span>

<span style="color: rgb(61, 61, 61);">Winter </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Term**</span>

<span style="color: #3d3d3d;">starting at 1. </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);">**<span style="color: rgb(61, 61, 61);">Duration</span>**

<span style="color: rgb(61, 61, 61);">1 Semester </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Language**</span>

<span style="color: rgb(61, 61, 61);">english</span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Level**</span>

<span style="color: rgb(61, 61, 61);">Master </span>

</td></tr></tbody></table>

<table border="1" id="bkmrk-verantwortung%3A-verwe" style="border-collapse: collapse; width: 100%; height: 124.8px; border-width: 1px; border-style: none;"><colgroup><col style="width: 24.7879%;"></col><col style="width: 75.2121%;"></col></colgroup><tbody><tr style="height: 35.4px;"><td style="height: 35.4px;">**Link:**</td><td style="height: 35.4px;">[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20262&veranstaltung.dtxt=Introduction+to+Simulation&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Introduction to Simulation")</td></tr><tr><td>**Limited accessability:**</td><td></td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Responsibility:**</td><td style="height: 29.8px;">Graham Horton </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Lecturer:**</td><td style="height: 29.8px;">Graham Horton </td></tr><tr><td>**Classes:**</td><td>- Lecture *Introduction to Simulation*
- Exercise class *Introduction to Simulation*

 </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Applicability in curriculum:**</td><td style="height: 29.8px;">- M.Sc. DKE: Fundamentals of Data Science  
- M.Sc. DE: Grundlagen Informatik  
- M.Sc. DE: Informatik Grundlagen (SPO 2027)  
  
</td></tr></tbody></table>

**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

<table border="1" id="bkmrk-studien-%2Fpr%C3%BCfungslei" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 32.0641%;"></col><col style="width: 34.9204%;"></col><col style="width: 33.0155%;"></col></colgroup><tbody><tr><td>**Pre-examination requirements:**</td><td>**Type of examination:**</td><td>**Teaching method / lecture hours per week (SWS):**</td></tr><tr><td></td><td>2h written exam

</td><td>- 2 SWS lecture
- 2 SWS exercise class

</td></tr></tbody></table>

<table border="1" id="bkmrk-voraussetzungen-nach" style="font-family: var(--font-body); font-size: 14px; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Prerequisites according to examination regulations:**</td><td>**Recommended prerequisites:**</td></tr><tr><td>keine

</td><td>Basic engineering mathematics

</td></tr></tbody></table>

<table border="1" id="bkmrk-medienformen%3A-litera" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Media:**</td><td>**Literature:**</td></tr><tr><td></td><td>Banks, Carson, Nelson, Nicol: Discrete-Event System Simulation

</td></tr></tbody></table>

**Comments:**

# Logik 2

**Winter**


(engl. Logic 2 )

<table bgcolor="#7FB3D9" border="1" id="bkmrk-%5Btitle%5D" style="width: 100%; border-collapse: collapse; border-width: 1px; border-style: none;"><colgroup><col style="width: 100%;"></col></colgroup><tbody><tr style="background-color: rgb(235, 243, 249);"><td>##### <span style="color: rgb(0, 104, 180);">**Modulnummer: FIN-INF-0** </span>

</td></tr></tbody></table>

<table bgcolor="#7FB3D9" border="1" id="bkmrk-" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 13.061%;"></col><col style="width: 15.442%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col></colgroup><tbody><tr><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Kürzel**</span>

<span style="color: rgb(61, 61, 61);">Logik2 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**CP**</span>

<span style="color: rgb(61, 61, 61);">10 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Semester**</span>

<span style="color: rgb(61, 61, 61);">Winter </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Fachsem.**</span>

<span style="color: #3d3d3d;">ab 4. </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);">**<span style="color: rgb(61, 61, 61);">Dauer</span>**

<span style="color: rgb(61, 61, 61);">1 Semester </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Sprache**</span>

<span style="color: rgb(61, 61, 61);">deutsch</span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Niveau**</span>

<span style="color: rgb(61, 61, 61);">Bachelor,Master </span>

</td></tr></tbody></table>

<table border="1" id="bkmrk-verantwortung%3A-verwe" style="border-collapse: collapse; width: 100%; height: 89.3907px; border-width: 1px; border-style: none;"><colgroup><col style="width: 20.2601%;"></col><col style="width: 79.7399%;"></col></colgroup><tbody><tr><td>**Link zum LSF:**</td><td>[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20262&veranstaltung.dtxt=Logik+2&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Logik 2")</td></tr><tr><td>**Zugang beschränkt:**</td><td>true </td></tr><tr style="height: 29.7969px;"><td style="height: 29.7969px;">**Verantwortung:**</td><td style="height: 29.7969px;">Fabian Neuhaus </td></tr><tr style="height: 29.7969px;"><td style="height: 29.7969px;">**Dozent:in:**</td><td style="height: 29.7969px;">Fabian Neuhaus </td></tr><tr><td>**Lehrveranstaltungen:**</td><td>Vorlesung Logik 2 Übung Logik 2 </td></tr><tr style="height: 29.7969px;"><td style="height: 29.7969px;">**Verwendbarkeit:**</td><td style="height: 29.7969px;">- 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)  
  
</td></tr></tbody></table>

**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)

  
<table border="1" id="bkmrk-studien-%2Fpr%C3%BCfungslei" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 32.6591%;"></col><col style="width: 35.9941%;"></col><col style="width: 31.3468%;"></col></colgroup><tbody><tr><td>**Prüfungsvorleistungen:**</td><td>**Studien-/Prüfungsleistungen:**</td><td>**Lehrform / SWS:**</td></tr><tr><td style="vertical-align: top;">- 2 Drittel der Übungsaufgaben votiert
- BA: mindestens 1 Präsentation
- MA: mindestens 2 Präsentationen

</td><td class="align-left" style="vertical-align: top;">Mündliche Prüfung

</td><td class="align-left" style="vertical-align: top;">Übung 2 SWS Vorlesung 2 SWS

</td></tr></tbody></table>

<table border="1" id="bkmrk-voraussetzungen-nach" style="font-family: var(--font-body); font-size: 14px; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Voraussetzungen nach Prüfungsordnung:**</td><td>**Empfohlene Voraussetzungen:**</td></tr><tr><td class="align-left" style="vertical-align: top;">keine

</td><td class="align-left" style="vertical-align: top;">Erfolgreicher Abschluss des Modul Logik bzw. fundiertes Wissen im Bereich der Themen des Moduls"Logik.

</td></tr></tbody></table>

<table border="1" id="bkmrk-medienformen%3A-litera" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Medienformen:**</td><td>**Literatur:**</td></tr><tr><td class="align-left" style="vertical-align: top;"></td><td class="align-left" style="vertical-align: top;"></td></tr></tbody></table>

**Hinweise:** Anmeldung via E-Learning-Plattform

# Machine Learning

##### Winter 


##### (in German: Machine Learning )

<table bgcolor="#7FB3D9" border="1" id="bkmrk-%5Btitle%5D" style="width: 100%; border-collapse: collapse; border-width: 1px; border-style: none;"><colgroup><col style="width: 100%;"></col></colgroup><tbody><tr style="background-color: rgb(235, 243, 249);"><td>##### <span style="color: rgb(0, 104, 180);">**Module-ID:** </span><span style="color: rgb(0, 104, 180);">**FIN-INF-120517** </span>

</td></tr></tbody></table>

<table bgcolor="#7FB3D9" border="1" id="bkmrk-" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 13.061%;"></col><col style="width: 15.442%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col></colgroup><tbody><tr><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Abbreviation**</span>

<span style="color: rgb(61, 61, 61);">ML </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Credit Points**</span>

<span style="color: rgb(61, 61, 61);">6 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Semester**</span>

<span style="color: rgb(61, 61, 61);">Winter </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Term**</span>

<span style="color: #3d3d3d;">starting at 1. </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);">**<span style="color: rgb(61, 61, 61);">Duration</span>**

<span style="color: rgb(61, 61, 61);">1 Semester </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Language**</span>

<span style="color: rgb(61, 61, 61);">english</span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Level**</span>

<span style="color: rgb(61, 61, 61);">Master </span>

</td></tr></tbody></table>

<table border="1" id="bkmrk-verantwortung%3A-verwe" style="border-collapse: collapse; width: 100%; height: 124.8px; border-width: 1px; border-style: none;"><colgroup><col style="width: 24.7879%;"></col><col style="width: 75.2121%;"></col></colgroup><tbody><tr style="height: 35.4px;"><td style="height: 35.4px;">**Link:**</td><td style="height: 35.4px;">[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20262&veranstaltung.dtxt=Machine+Learning&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Machine Learning")</td></tr><tr><td>**Limited accessability:**</td><td></td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Responsibility:**</td><td style="height: 29.8px;">Andreas Nürnberger </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Lecturer:**</td><td style="height: 29.8px;">Andreas Nürnberger </td></tr><tr><td>**Classes:**</td><td>- Lecture *Machine Learning*
- Exercise class *Machine Learning*

 </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Applicability in curriculum:**</td><td style="height: 29.8px;">- 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  
  
</td></tr></tbody></table>

**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

<table border="1" id="bkmrk-studien-%2Fpr%C3%BCfungslei" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 32.0641%;"></col><col style="width: 34.9204%;"></col><col style="width: 33.0155%;"></col></colgroup><tbody><tr><td>**Pre-examination requirements:**</td><td>**Type of examination:**</td><td>**Teaching method / lecture hours per week (SWS):**</td></tr><tr><td></td><td>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.

</td><td>- Lecture (2 SWS)
- Exercise class (2 SWS)

</td></tr></tbody></table>

<table border="1" id="bkmrk-voraussetzungen-nach" style="font-family: var(--font-body); font-size: 14px; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Prerequisites according to examination regulations:**</td><td>**Recommended prerequisites:**</td></tr><tr><td>keine

</td><td>Algorithmen und Datenstrukturen

</td></tr></tbody></table>

<table border="1" id="bkmrk-medienformen%3A-litera" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Media:**</td><td>**Literature:**</td></tr><tr><td></td><td></td></tr></tbody></table>

**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 )

<table bgcolor="#7FB3D9" border="1" id="bkmrk-%5Btitle%5D" style="width: 100%; border-collapse: collapse; border-width: 1px; border-style: none;"><colgroup><col style="width: 100%;"></col></colgroup><tbody><tr style="background-color: rgb(235, 243, 249);"><td>##### <span style="color: rgb(0, 104, 180);">**Module-ID:** </span><span style="color: rgb(0, 104, 180);">**FIN-INF-120502** </span>

</td></tr></tbody></table>

<table bgcolor="#7FB3D9" border="1" id="bkmrk-" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 13.061%;"></col><col style="width: 15.442%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col></colgroup><tbody><tr><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Abbreviation**</span>

<span style="color: rgb(61, 61, 61);">PPSW </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Credit Points**</span>

<span style="color: rgb(61, 61, 61);">6 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Semester**</span>

<span style="color: rgb(61, 61, 61);">Summer </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Term**</span>

<span style="color: #3d3d3d;">starting at 1. </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);">**<span style="color: rgb(61, 61, 61);">Duration</span>**

<span style="color: rgb(61, 61, 61);">1 Semester </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Language**</span>

<span style="color: rgb(61, 61, 61);">english</span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Level**</span>

<span style="color: rgb(61, 61, 61);">Master </span>

</td></tr></tbody></table>

<table border="1" id="bkmrk-verantwortung%3A-verwe" style="border-collapse: collapse; width: 100%; height: 124.8px; border-width: 1px; border-style: none;"><colgroup><col style="width: 24.7879%;"></col><col style="width: 75.2121%;"></col></colgroup><tbody><tr style="height: 35.4px;"><td style="height: 35.4px;">**Link:**</td><td style="height: 35.4px;">[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20261&veranstaltung.dtxt=Principles+and+Practices+of+Scientific+Work+and+Soft+Skills&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Principles and Practices of Scientific Work and Soft Skills")</td></tr><tr><td>**Limited accessability:**</td><td>true </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Responsibility:**</td><td style="height: 29.8px;">PD Dr. Claudia Krull </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Lecturer:**</td><td style="height: 29.8px;">PD Dr. Claudia Krull </td></tr><tr><td>**Classes:**</td><td>- Lecture PPSW
- Exercise PPSW

 </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Applicability in curriculum:**</td><td style="height: 29.8px;">- 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)  
  
</td></tr></tbody></table>

**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 &amp; Self Management
- Project Management &amp; 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

<table border="1" id="bkmrk-studien-%2Fpr%C3%BCfungslei" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 32.0641%;"></col><col style="width: 34.9204%;"></col><col style="width: 33.0155%;"></col></colgroup><tbody><tr><td>**Pre-examination requirements:**</td><td>**Type of examination:**</td><td>**Teaching method / lecture hours per week (SWS):**</td></tr><tr><td>participation in all classes  
completion of assignments  
keeping all deadlines

</td><td>Report

</td><td>Lecture (2 SWS) Exercise (2 SWS)

</td></tr></tbody></table>

<table border="1" id="bkmrk-voraussetzungen-nach" style="font-family: var(--font-body); font-size: 14px; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Prerequisites according to examination regulations:**</td><td>**Recommended prerequisites:**</td></tr><tr><td>keine

</td><td></td></tr></tbody></table>

<table border="1" id="bkmrk-medienformen%3A-litera" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Media:**</td><td>**Literature:**</td></tr><tr><td></td><td></td></tr></tbody></table>

**Comments:** participation is limited to 40