Module Handbook

  • Dynamischer Default-Fachbereich geändert auf SO

Notes on the module handbook of the department Social Sciences

Die hier dargestellten Studiengang-, Modul- und Kursdaten des Fachbereichs Sozialwissenschaften [SO] befinden sich noch in Entwicklung und sind nicht offiziell.

Die offiziellen Modulhandbücher finden Sie unter https://www.sowi.uni-kl.de/studium/ .

Module SO-09-120-M-6

Interdisciplinary cross-section (M, 12.0 LP)

Module Identification

Module Number Module Name CP (Effort)
SO-09-120-M-6 Interdisciplinary cross-section 12.0 CP (360 h)

Basedata

Notice

Mindestens eine Veranstaltung aus dem Modul wählen, eine weitere Veranstaltung aus dem Angebot aus dem Angebot der TUK wählen.

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
4V+2U INF-75-50-K-5
Machine Learning I - Theoretical Foundations
WP 84 h 156 h
TEILN
ja PL1 8.0 SuSe
4V+2U INF-00-12-K-2
Information Systems
WP 84 h 156 h
TEILN
ja PL1 8.0 SuSe
2V+1U INF-00-31-K-3
Web 2.0 Technologies 1 (Principles and Techniques)
WP 42 h 78 h
TEILN
ja PL1 4.0 WiSe
2V+1U INF-00-32-K-3
Web 2.0 Technologies 2 (Services, Security and Privacy)
WP 42 h 78 h
TEILN
ja PL1 4.0 SuSe
3V+1U WIW-VWL-SPT-K-1
Game Theory
WP 60 h 120 h
TEILN
ja PL1 6.0 SuSe
3V+1U WIW-VWL-MIK-K-1
Microeconomics
WP 60 h 120 h
TEILN
ja PL1 6.0 WiSe
3K WIW-IOE-BE-K-7
Behavioural Economics
WP 45 h 90 h
TEILN
ja PL1 4.5 WiSe
2S SO-08-26.2300-K-7
Learning and Behavior
WP 28 h 62 h
TEILN
ja PL1 3.0 irreg. WiSe
2S SO-08-26.2100-K-6
Judgement and Decision-Making
WP 28 h 62 h
TEILN
ja PL1 3.0 SuSe
  • About [INF-75-50-K-5]: Title: "Machine Learning I - Theoretical Foundations"; Presence-Time: 84 h; Self-Study: 156 h
  • About [INF-75-50-K-5]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [INF-00-12-K-2]: Title: "Information Systems"; Presence-Time: 84 h; Self-Study: 156 h
  • About [INF-00-12-K-2]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [INF-00-31-K-3]: Title: "Web 2.0 Technologies 1 (Principles and Techniques)"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-00-31-K-3]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [INF-00-32-K-3]: Title: "Web 2.0 Technologies 2 (Services, Security and Privacy)"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-00-32-K-3]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [WIW-VWL-SPT-K-1]: Title: "Game Theory"; Presence-Time: 60 h; Self-Study: 120 h
  • About [WIW-VWL-SPT-K-1]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [WIW-VWL-MIK-K-1]: Title: "Microeconomics"; Presence-Time: 60 h; Self-Study: 120 h
  • About [WIW-VWL-MIK-K-1]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [WIW-IOE-BE-K-7]: Title: "Behavioural Economics"; Presence-Time: 45 h; Self-Study: 90 h
  • About [WIW-IOE-BE-K-7]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [SO-08-26.2300-K-7]: Title: "Learning and Behavior"; Presence-Time: 28 h; Self-Study: 62 h
  • About [SO-08-26.2300-K-7]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.
  • About [SO-08-26.2100-K-6]: Title: "Judgement and Decision-Making"; Presence-Time: 28 h; Self-Study: 62 h
  • About [SO-08-26.2100-K-6]: The study achievement "[TEILN] continuous and active participation in the courses" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: examination in form of partial achievements
  • Examination Frequency: each semester

Evaluation of grades

All partial module examinations have to be passed. The module grade is the arithmetic mean of all partial examination grades.


Contents

  • Introduction and Overview
  • Linear classifiers
  • Support vector machines
  • Optimization
  • Kernel methods
  • Deep learning
  • Regularization and Overfitting
  • Regression
  • Clustering
  • Dimensionality reduction
  • Random forests
  • Introduction and Basics
  • Introduction to Information Retrieval (Vector Space Model, TF*IDF)
  • Models for Result Quality (Precision and Recall)
  • Latent-Topic-Models (Singular Value Decomposition, LSI)
  • Computation of Document Similarities (Shingling)
  • Link Analysis and Markov Chains (PageRank)
  • Data-Mining: Frequent-Itemset-Mining and Clustering (k-Means)
  • Entity Relationship Modeling
  • The Relational Model
  • Relational Design Theory (Normal Forms)
  • Rule-based Conjunctive Queries and Relational Calculus
  • The SQL language (incl. recursion and window queries)
  • Relational Algebra and Extensions (Aggregation, Duplicate Elimination, Bag Semantics)
  • Views, Data Integrity, and Access Control
  • Programming Principles of SQL-based Applications (JDBC)
  • Database Triggers and User-Defined Functions
  • DBS Architecture and Buffer Replacement Strategies
  • Efficient Data Access through Indices (B/B+ Trees, Hashing, Bulkloading)
  • Equivalence Rules of Relational Algebra (Logical Query Optimization and Selectivity Estimation)
  • Transactions (ACID) and Serializability
  • Selected topics of managing Big Data (NoSQL, CAP Theorem, Eventual Consistency)
  • HTTP (web-standards, protocols, request and response analysis, authentication, cookies)
  • HTML5 (concepts, standards, encoding, semantical structures)
  • CSS (concepts, properties, selectors, cascade)
    • Layouts (box-model, positioning, flexbox, grid)
    • Webdesign (concepts, adaptive designs, animation)
  • Javascript (concepts, language introduction, APIs, DOM, event-handling, jQuery)
  • PHP (concepts, language introduction, APIs)
  • Information systems (relational databases, integrity constraints, modelling)
  • SQL (simple and complex queries, schema definition, transactions, integrity).
  • PHP (MySQL API, security, SQL injections)
  • Web application framework Django:
    • Basics of Python, OR mapper, interactive shell.
    • Introduction to Django, schema definition, schema migration, relations, querysets, admin interface
    • Request processing, templates, transactions, URL mapper, parameter processing, form processing, authentication, authorisation
  • Javascript APIs (DOM manipulation, event handling, jQuery, asynchronous communication, AJAX, JSON)
  • Security ( attack vectors, protection measures, MITM, transport encryption, X509-PKI, cookie and session stealing, session fixation, cross-site request forgeries)
  • Data protection and privacy (user tracking, branding, privacy, DSGVO)
  • Static Games of Complete Information
  • Static Games of Incomplete Information
  • Dynamic Games of Complete Information
  • Dynamic Games of Incomplete Information
  • Cooperative Game Theory: Nash-Bargaining Solution
  • Illustrative Applications
Introduction to Economics

Consumer Choice

  • Demand, comparative statics
  • Labour supply
  • Decisions under uncertainty
  • Intertemporal decisions

Production

  • Short-term and long-term costs
  • Profit maximization
  • Supply Market

Equilibrium

  • Partial and general equilibrium
  • Market entry
  • Welfare

Market Failure and Externalities

Public Goods and Taxes

Introduction to Game Theory

Describing observed behaviour in experiments and human decision making

by using concepts of Behavioural Economics

Central Topics:

  • Need for Behavioural Economics, its development over the last

decades and its relevance for modern economics

  • Choice under certainty
  • Judgment under risk and uncertainty
  • Choice under risk and uncertainty
  • Intertemporal choices
  • Strategic interaction
  • Applications to the finance industry, to the insurance industry and to the

labour market

  • Behavioral and cognitive theories of animal and human learning, skills and procedural learning
  • neural basis of learning and behavior
  • interaction between cognition, motivations and emotion
Probability and judgment
  • models of individual and group decision making, including choices between complex options involving risk and time
  • reasoning with uncertainty
  • methods of measurement
  • practical implication

Competencies / intended learning achievements

Es wird/werden in diesem Modul schwerpunktmäßig folgende Kompetenz/en gefördert:

− Einordnung der im Studiengang erworbenen Kenntnisse in einen interdisziplinären Kon- text,

− Fundierung der Modelle und Analysen durch ökonomische und psychologische Entschei- dungstheorien

Mit erfolgreichem Abschluss des Moduls sind die Studierenden in der Lage,

− Probleme des maschinellen Lernens im Alltag oder Arbeitsalltag zu erkennen

− Lösungen für ML-Probleme zu finden und zu implementieren

− Das Funktionieren von ML-Algorithmen zu verstehen

− Konzepte maschinellen Lernens als einen generischen Absatz für eine Vielzahl von Diszipli-

nen zu beschreiben (Bildverarbeitung, Robotik, Computerlinguistik und Software-Enginee-

ring.

− Nutzung von Informations- und Datenmodellen zur Modellierung von Miniwelten,

− Bewertung und Verbesserung der Güte von Modellierungsergebnissen,

− Aufbau, Wartung und Abfrage von Datenbanken mit Hilfe von deklarativen, standardisierten

Anfragesprachen und

− Sicherung der Abläufe in Datenbanken durch das Transaktionskonzept.

− die Möglichkeiten, Ziele und zur Realisierung eingesetzten grundlegenden Techniken,

Schnittstellen und Protokolle im Web 2.0 (HTTP, HTML 5, CSS3, Javascript, PHP) zu verste-

hen,

− aktuelle Web-Standards zu interpretieren und umzusetzen,

− semantische Webseiten mit fortgeschrittenen Gestaltungsmöglichkeiten zu designen und

− einfacher Web-Services zu realisieren

− fortgeschrittene serverseitigen Web-Dienste zu konzipieren, planen und realisieren

− fortgeschrittene clientseitige aktive und interaktive Anwendungen zu konzipieren, planen

und realisieren

References to Module / Module Number [SO-09-120-M-6]

Course of Study Section Choice/Obligation
[SO-88A.?-SG#2021] M.A. Soziologie und empirische Sozialforschung mit Schwerpunkt Computational Social Science [2021] [Specialisation] Wahlpflicht [WP] Compulsory Elective