Einleitung
Der Modulkatalog hilft Ihnen bei der Planung Ihres Studiums, indem er Ihnen
eine Übersicht über alle Module Ihres Studienprogramms bietet.
Das Dokument enthält folgende Rubriken:
Der Modulkatalog ist ein Informationsdokument und nicht rechtsverbindlich.
Zu Beginn jedes Semesters wird eine aktuelle Version auf der Website der
Philosophischen Fakultät publiziert.
Übersicht über die Modulgruppen
Übersicht über die Module in den Modulgruppen
06M-7523i01Scientific Specialization
Module in der Gruppe: Scientific Specialization
|
Diese Modulgruppe enthält ausschliesslich Wahlmodule.
Informieren Sie sich im Vorlesungsverzeichnis über das aktuelle Angebot. |
|
|
06M-7523i02Core Modules of Computational Linguistics and Language Technology
06M-7523i03Computer Science
06M-7523i04Computational Linguistics and Language Technology in Practice
Katalog der Pflichtmodule, Wahlpflichtmodule und Wahlmodule
Der Katalog enthält Informationen zu jedem Pflicht- und Wahlpflichtmodul.
Zum Teil finden Sie auch Informationen zu Wahlmodulen (Modultitel in eckigen
Klammern). Beachten Sie, dass die Titel von Wahlmodulen semesterweise wechseln
können und dass oft weitere, nicht im Modulkatalog enthaltene Wahlmodule
angeboten werden. Diese und alle anderen semesterbezogenen Informationen
(wie Veranstaltungstitel, Termine, Dozierende, Informationen zur Buchung)
entnehmen Sie dem aktuellen Vorlesungsverzeichnis.
06SM523-501 Advanced Techniques of Machine Translation
Moduldetails: 06SM523-501 Advanced Techniques of Machine Translation
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
In this course we present and experience the latest research in Machine Translation. Topics include building and evaluating Machine Translation systems, and integrating the systems into various application scenarios. We take a broad perspective and look at Machine Translation for different language situations (written, spoken, and signed language). And we take a deep perspective by studying the underlying linguistic knowledge sources and statistical techniques in detail. |
| Lernziel |
The students (1) will be acquainted with the latest research and developments in Machine Translation (2) will learn how to build Machine Translation systems with state-of-the-art performance (3) will learn how to perform Machine Translation experiments and publish the results |
| Unterrichtssprache |
Englisch |
| Voraussetzungen |
Basic knowledge in Machine Translation and Machine Learning. |
| Leistungsnachweis |
Portfolio (75% final exam und 25% exercises) |
| Notenskala |
1-6, in Halbschritten |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-505 Machine Learning for Natural Language Processing 1
Moduldetails: 06SM523-505 Machine Learning for Natural Language Processing 1
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Tutorat, Vorlesung |
| Allgemeine Beschreibung |
Modern Natural Language Processing (NLP) requires a high level of expertise in neural machine learning techniques. This course first covers the basic supervised and unsupervised methods used in NLP. The second part focuses on transfer learning and prediction of linguistic structures. Participants will gain theoretical and practical experience in this course. |
| Lernziel |
Students know about relevant machine learning techniques for NLP. They understand advanced neural methods for transfer learning and linguistic structure prediction. They gain practical experience in applying machine learning to NLP problems. |
| Unterrichtssprache |
Englisch |
| Voraussetzungen |
Good programming skills in Python and basic knowledge in statistics and probability theory. |
| Leistungsnachweis |
Portfolio (75% written exam and 25% proof of academic achievements in self-study) |
| Notenskala |
1-6, in Halbschritten |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-506 Machine Learning for Natural Language Processing 2
Moduldetails: 06SM523-506 Machine Learning for Natural Language Processing 2
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Tutorat, Vorlesung |
| Allgemeine Beschreibung |
This course focuses on current neural machine learning (ML) methods that achieve state-of-the-art performance in Natural Language Processing (NLP) tasks. Participants study and present current research articles from the NLP literature. As a practical preparation for a modern empirical master thesis, they learn how to plan, conduct and evaluate ML-based NLP experiments and how to describe their approach and results in a scientific paper. |
| Lernziel |
Students know the current state of machine learning methods for various NLP tasks. They know how to conduct machine learning-based empirical research in computational linguistics and how to present it in the scientific format of a workshop paper. |
| Unterrichtssprache |
Englisch |
| Voraussetzungen |
Successfully completed module «Machine Learning for Natural Language Processing I».
This module is open only to Master's students. It may not be booked by Bachelor's students as a pre-Master's module. |
| Leistungsnachweis |
Portfolio: 50% Referat/Diskussionsbeiträge, 50% schriftliche Arbeit |
| Notenskala |
1-6, in Halbschritten |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Frühjahrssemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-519 Fundamentals of speech sciences and signal processing
Moduldetails: 06SM523-519 Fundamentals of speech sciences and signal processing
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung |
| Allgemeine Beschreibung |
Experience the captivating world of speech signal processing. Discover the essential techniques that enable us to decode, manipulate, and reproduce the human communication with speech. Learn about signal and system theory necessary for speech processing in both human interaction and cutting-edge technological applications. This lecture series will equip you with the fundamental knowledge needed to unravel the intricacies of speech communication and embrace the possibilities it holds. |
| Lernziel |
(1) Fundamental skills in speech signal processing (2) Understanding of speech acoustics like signal types, signal transformations, acoustic systems, signal and system theory (3) Application of the signal processing techniques in research and industrial products. |
| Unterrichtssprache |
Englisch |
| Voraussetzungen |
An interest in speech signal processing with computers is required. |
| Leistungsnachweis |
Portfolio: (a) weekly assignments, 40% (b) end of term exam, 60% |
| Notenskala |
1-6, in Halbschritten |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Frühjahrssemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-520 Instrumental techniques of phonetic research
Moduldetails: 06SM523-520 Instrumental techniques of phonetic research
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Übung |
| Allgemeine Beschreibung |
Since speech is a transient event, phoneticians regularly resort to the aid of technical devices in order to record, describe and analyse the production, the acoustics and the perception of speech sounds. Hence, in this module we look at the technical side of phonetic research and the students acquire and develop skills and techniques necessary for the successful deployment of such devices, ranging from sound recording equipment (especially recorders and microphones) to more specialized phonetic equipment (such as the laryngograph) to software solutions geared specifically towards the need of phoneticians (such as Praat or the R-package 'vowels'). |
| Lernziel |
Students know how to make high-quality audio recordings for phonetic research purposes. They can annotate sound files, make reliable measurements in them (formants, pitch, intensity, etc.) and produce meaningful visualizations (wave forms, spectra, spectrograms, etc.) with suitable software. They also understand how to read spectrograms so as to draw informed conclusions about the temporal and spectral characteristics of speech events. Moreover, students understand the most important key notions and concepts in automatizing measurements and in making them replicable (scripting). |
| Unterrichtssprache |
Englisch |
| Voraussetzungen |
Students are required to have attended an introductory module in phonetics at bachelor or master level. |
| Leistungsnachweis |
During the semester students run guided analyses on spoken material both as part of the course but also as personal homework. In addition students are recquired to hand in a small-scale empiric study (7-10 pages) to be handed in a fortnight after the last meeting of the semester. Both their analyses during the semester and their final report form their portfolio and thus the basis for the evaluation of their performance. |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Frühjahrssemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-530 Eye Tracking and NLP
Moduldetails: 06SM523-530 Eye Tracking and NLP
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Übung |
| Allgemeine Beschreibung |
This course introduces a growing research area that combines eyetracking during reading with natural language processing (NLP).
Students will learn how eye movements in reading can be leveraged for enhancing and interpreting language models, and how eye movements can be exploited for a range of new human-centered applications. The course covers the following topics: (i) fundamentals of eye movements in reading, (ii) experimental methodologies and available data sets, (iii) generative models of eye movements, iv) leveraging eye movement data for NLP and v) human-centered applications. |
| Lernziel |
Students will acquire theoretical knowledge and develop practical skills in the topics covered by this course. |
| Unterrichtssprache |
siehe Vorlesungsverzeichnis |
| Voraussetzungen |
Python programming skills at least on the level of the Module «Programmiertechniken in der Computerlinguistik
2», and familiaritywith foundational knowledge in machine learning and NLP. |
| Leistungsnachweis |
Portfolio (80% written exam und 20% theoretical and practical assignments) |
| Notenskala |
1-6, in Halbschritten |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Frühjahrssemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-532 Artificial Intelligence for Language Accessibility
Moduldetails: 06SM523-532 Artificial Intelligence for Language Accessibility
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Blind persons and persons with visual impairments, deaf persons and persons with hearing impairments, persons with cognitive impairments, motor impairments, and persons with speech and language disorders face many barriers in their everyday lives, often related to language. This course provides an overview of common barriers and introduces artificial intelligence approaches developed to reduce some of these barriers.
Specifically, the course deals with tasks such as sign language recognition, translation, and production; intralingual subtitling; audio description; diagnostics of speech and language disorders; automatic text simplification; and speech recognition and synthesis as part of Augmentative and Alternative Communication (AAC) and Ambient Assisted Living (AAL). A focus is on research approaches; transversal topics are those of multimodality and ethics. Students will gain hands-on practice applying some of the approaches as part of the exercises accompanying the course.
This course is preceded by a "Digital Accessibility" course on Bachelor's level. |
| Lernziel |
Students (1) are aware of different target groups in the context of accessibility; (2) are aware of language barriers that these target groups face; (2) know about research approaches from the area of artificial intelligence towards reducing some of these barriers; (3) know how to apply a selection of these approaches. |
| Unterrichtssprache |
Englisch |
| Voraussetzungen |
Knowledge to the extent of the courses "Einführung in die Computerlinguistik
1" and "Programmiertechniken der Computerlinguistik
1"; familiarity with model training |
| Leistungsnachweis |
Written exam |
| Notenskala |
1-6, in Halbschritten |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-533 Advanced Machine Learning
Moduldetails: 06SM523-533 Advanced Machine Learning
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Tutorat, Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
This course examines advanced methods in supervised and unsupervised machine learning, with a focus on deep learning architectures. Topics include state-of-the-art models such as Transformers, Graph Neural Networks, Diffusion Models, and State Space Models, with attention to both their theoretical principles and practical implementation. |
| Lernziel |
Students will acquire theoretical knowledge of state-of-the-art machine learning techniques and the practical skills to apply these methods to different kinds of problem settings. |
| Unterrichtssprache |
siehe Vorlesungsverzeichnis |
| Voraussetzungen |
Solid knowledge of supervised and unsupervised machine learning, probability theory, linear algebra, multivariate calculus as well as fluent Python programming skills are required. |
| Leistungsnachweis |
Portfolio (20% practical assignments, 80% written exam) |
| Notenskala |
1-6, in Halbschritten |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Frühjahrssemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-534 Introduction to Forensic Speech Sciences
Moduldetails: 06SM523-534 Introduction to Forensic Speech Sciences
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Forensic Speech Science is a multidisciplinary field that applies various aspects of phonetics, linguistics, signal processing, and automatic speaker recognition for legal and investigative purposes. This module aims to introduce the goals, tasks (e.g. transcription, speaker comparison, disambiguation of disputed utterances) and practices of forensic speech and audio analysis. This module blends frontal teaching and hands-on sessions. |
| Lernziel |
By the end of this module, students will have achieved the following learning objectives: - A fundamental understanding of factors affecting the perception, analysis, and transcription of speech signals within investigative settings. - Develop familiarity with diverse methods for transcribing forensic audio materials, including using state-of-the-art automatic speech recognition systems. - Gain familiarity with multiple approaches to forensic voice comparison, including auditory assessment, acoustic-phonetic analysis, and automatic techniques. - Showcase their abilities through practical demonstrations in voice comparison and the transcription of forensic recordings |
| Unterrichtssprache |
Englisch |
| Voraussetzungen |
The participation to modules on Phonetics and Phonology is highly recommended. |
| Leistungsnachweis |
Portfolio: 50% assignments, 50% final course exam |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-s15 [Excursion]
Moduldetails: 06SM523-s15 [Excursion]
| Modulgruppe |
Core Modules of Computational Linguistics and Language Technology |
| Modultyp |
Wahl |
| ECTS |
3 |
| Lehrform |
Exkursion |
| Allgemeine Beschreibung |
Excursions, similar to practical training off-site, offer the opportunity to gain insight into the daily work of computional linguists. In contrast to these, however, the emphasis is not on concrete work in a company, but on the ability to recognize and assess the problems and methods of a field of application of computational linguistics in direct contact as accurately as possible. This provides insights into problem areas, allows one to measure one's own specific interests and, if necessary, to fix future work areas and employers. The students prepare for the excursion by effectively researching and studying relevant literature. This module can be booked to credit the participation in excursions. |
| Lernziel |
The students (1) gain insight into language technology companies and university or non-university research departments (2) get to know the theory and practice of computational linguistics in a concrete example (3) find or develop one's own specific interests (4) gain the ability to get insight into practical problems and methods through interviews with practitioners (5) get in contact with potential employers |
| Unterrichtssprache |
siehe Vorlesungsverzeichnis |
| Voraussetzungen |
This module can not be booked by the students themselves, the booking has to be authorized by the module coordinator. In order to credit the participation in an excursion, it is essential to contact to the module coordinator before the start of the excursion. |
| Leistungsnachweis |
Nachweis von im Selbststudium erbrachten Studienleistungen |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
keine Wiederholungsmöglichkeit |
| Angebotsmuster |
1-semestrig (einmalig) |
| Organisation |
Institut für Computerlinguistik |
03SM22BI0001 Foundations of Computing II (L+E)
Moduldetails: 03SM22BI0001 Foundations of Computing II (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Required second-year course covering topics from discrete math and formal methods building the foundations of computing. The material of this course is pervasive in the areas of algorithms, data structures and programming but appears virtually in all areas of computer science as well. The course will cover topics such as, but not limited to, proof methods, formal languages, deterministic and nondeterministic finite automata, grammars and pushdown automata, Turing machines, computability, decidability and complexity, P and NP, NP-completeness. |
| Lernziel |
The goal of the course is to familiarize the student with formal methods of computing and their value for computer science and related disciplines, and to provide basic training in applying formal methods to many different kinds of problems. Students should learn the fundamental limits of computation and extend their knowledge on formal languages as well as on formal programming models. Principles of interference, deduction, induction and contradiction should regularly be applied to demonstrate the formal correctness of models and limits. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22BI0003 Numerical Methods in Informatics (L+E)
Moduldetails: 03SM22BI0003 Numerical Methods in Informatics (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
The course presents the basic numerical and linear algebra techniques to solve mathematical problems that arise in computer science. The topics cover a wide range such as e.g.: basic concepts of scientific programming, solution of systems of linear equations and of nonlinear equations; interpolation and least-square approximation of data and functions; eigenvalues and eigenvectors computation; integration and differentiation and numerical optimization.
The course consists of lectures, exercises and homework assignments. |
| Lernziel |
By the end of the course, the students will be able to identify a suitable method to solve basics problems of scientific computing, understand the main implications of the method and implement it directly or apply it using existing libraries. The students will learn how to solve such problems and to implement required algorithms and solutions in Python. The course will provide to the students the basis to understand more complex numerical tools that they may encounter in future courses or in their professional career. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22BI0004 Software Construction (L+E) (Softwarekonstruktion)
Moduldetails: 03SM22BI0004 Software Construction (L+E) (Softwarekonstruktion)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Knowing how to program does not make a student a software designer. The next step involves learning and practicing the fundamental principles and techniques for designing long-lived software systems. This course helps students learn software design by building examples of small versions of tools that programmers use every day. The course includes a practical component, highlighting the
engineering skills needed to design robust software systems. Primarily, the course is taught using Python, with the final lectures introducing Java. |
| Lernziel |
As a result of this course, students will acquire:
A solid understanding of the principles and techniques of modern software design, including:
A.1 Concepts and issues of software quality and maintainability
A.2 Code as data
A.3 Object-oriented programming
A.4 Fundamental design patterns (recognized best practices of software architectures)
A.5 Fundamentals of software testing
A.6 Fundamentals of modern software engineering tools
B. Experience in collaborative tasks applying these principles and techniques |
| Unterrichtssprache |
English |
| Voraussetzungen |
Informatics I (or equivalent) |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22BI0005 Wirtschaftsinformatik II (V+Ü) (Business Informatics II)
Moduldetails: 03SM22BI0005 Wirtschaftsinformatik II (V+Ü) (Business Informatics II)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
- Die Vorlesung behandelt Prozessmanagement und ERP Systeme. Sie hat folgenden Aufbau: Einführung in die Lehrveranstaltung
- Prozesse
- Business Process Model and Notation (BPMN)
- Strategisches Prozessmanagement
- Ist/Soll-Modellierung
- Implementierung
- Process Mining
- Enterprise Resource Planning (ERP)
- Organisatorische Implementierung
- Die begleitenden Übungen behandeln das Modellieren von BPMN, die Ist/Sollmodellierung, die Implementierung mit einer Process Engine sowie die Nutzung von ERPSim. Projektaufgabe.
|
| Lernziel |
Lernziel 1: Betrieblich Prozesse analysieren, modellieren, implementieren und managen können.
Lernziel 2: ERP Systeme nutzen und implementieren können. (ERP = Enterprise Ressource Planning) |
| Unterrichtssprache |
Deutsch; Hintergrundliteratur und Software kann auf Englisch sein |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22BI0006 Computer Networks and Distributed Systems (L+E) (Kommunikationsnetze und Verteilte Systeme)
Moduldetails: 03SM22BI0006 Computer Networks and Distributed Systems (L+E) (Kommunikationsnetze und Verteilte Systeme)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
The Information and Communications Technology (ICT) age has arrived within our daily life, not only during work and business hours, but at a good deal of entertainment and social interactions, too. Thus, the society has to cope with such
developments of digitization. Many of those human-centric statements only refer to or try to analyze the impact of these changes and the society. However, in very many cases the fundamentals to derive reliable, correct, and transparent conclusions requires a detailed know-how of Communication Networks and Distributed Systems (CNDS). Therefore, once stand-alone systems are discussed, their interconnection across physical boundaries of an office or building site forms the major development of the ICT society. While fundamental communication architectures did introduce communications by technical means, achieved over the past 100 years, the development of telephone communications to today's Internet will be covered. Protocols, reliable, unreliable, and secure services, algorithms for finding the corresponding receiver, routing, and basic mechanisms for Internet operations will form this lecture's part one.
Furthermore, once stand-alone systems have been interconnected, they constitute Distributed Systems, which form a collection of independent computers that appear to their users as a single coherent system, embedding hardware, within which all machines are fully autonomous, and software, for which users think they deal with a single system. Thus, basic theory and techniques of Distributed Systems are covered in this lecture's part two. Driven by an introduction, naming principles and distributed file systems are outlined. To ensure an application-driven interoperability, approaches for synchronization and coordination are discussed. Examples of Distributed Systems in use are overviewed. Finally, part three will overview the role of security in Computer Networks and Distributed Systems concludes this class. |
| Lernziel |
Students will receive the required insights into basic foundations on Communication Networks and Distributed Systems. More specifically, the lecture will teach communication architectures, network building blocks, shared links, packet switching, end-to-end protocols, selected Internet applications, naming principles, distributed file systems synchronization, coordination, and basic security elements as well as mechanisms. Overall, students will be able to understand which communication systems exist, how Internet-based systems operate world-wise, which communications can be reliable, how the basic inter-operations of Distributed Systems work, and which ones may be secured. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22BMI003 Requirements Engineering I (L+E)
Moduldetails: 03SM22BMI003 Requirements Engineering I (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
3 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Specifying requirements is a crucial prerequisite for successful software development.
This course gives an introduction to the principles, practices, languages, methods, processes, and tools for specifying and managing requirements. |
| Lernziel |
The students acquire basic knowledge, understanding and skills in the core principles, practices, languages, methods, and processes of Requirements Engineering. |
| Unterrichtssprache |
English |
| Voraussetzungen |
Basic knowledge of software development and modeling. Having taken a course in Software Engineering or read a SE textbook is strongly recommended.
Students enrolled in the BSc in Informatics program must have passed the assessment level successfully. |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0001 Information Management (L+E)
Moduldetails: 03SM22MI0001 Information Management (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
This lecture covers the management challenges and opportunities posed by information systems and offers methods to solve those problems. After completion of this course, the student is able to describe the problems and the tasks related to IT management, to explain these problems and tasks and to solve example tasks.
Topics covered:
1. Intro, IT and Strategy
2. Business Models
3. IT Outsourcing
4. IT Governance + IT Organization
5. Portfolios
6. Architectures
7. IT Projects
8. IT Benefits Management
9. Agile IT
10. IT Service Management
11. ITIL
12. Oracle Guest Lecture
13. DigitalOrganizations
14. Future of Work
Exercises will include case studies, small projects and paper reading.
There will be guest lectures. |
| Lernziel |
This lecture covers the management challenges and opportunities posed by information systems and offers methods to solve those problems. After completion of this course, the student is able to describe the problems and the tasks related to IT management, to explain these problems and tasks and to solve example tasks. Furthermore, students will be able to critically reflect on information management literature. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0002 Fundamentals of Software Systems (L+E)
Moduldetails: 03SM22MI0002 Fundamentals of Software Systems (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Introduction to advanced topics pertaining to the development and evolution of software systems, particularly distributed, data-intensive systems. |
| Lernziel |
The students will acquire and be able to apply knowledge on:
- Encoding and Distributed Systems
- Unreliable Communication Systems
- Consistency and Consensus in Distributed Systems
- Distributed Concurrency Control
- Distributed Reliability
- The Technical Evolution of Software
- Software Architecture
- Social Aspects of software development
- Open Source, Sustainability and Inclusion
This will enable students to develop and analyze on their own, at a later stage, maintainable, efficient, performing, and reliable distributed software systems. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0003 Fundamentals of Human-Centered Computing
Moduldetails: 03SM22MI0003 Fundamentals of Human-Centered Computing
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
This course is an introductory module for Human-Centered Computing.
Students will gain learn knowledge and skills in individual and collaborative work, to work with scholarly literature, and to conduct scholarly discourses.
They will learn concepts and processes from cognitive psychology and how to apply them to improve their thinking and work by themselves and with others.
Students will learn several conceptual frameworks that could help them understand and assess research contributions. They will learn about components and forms of arguments and critiques. This course will use the scholarly literature from various fields related to Human-Centered Computing. |
| Lernziel |
1. Students understand concepts and processes in cognitive psychology and can articulate how these theories apply to work situations.
2. Students know conceptual frameworks for understanding and assessing research contributions.
3. Students can identify the primary contributions of research papers.
4. Students can assess the credibility of sources of scholarly publications.
5. Students can analyze scholarly arguments and assess their quality.
6. Students can synthesize knowledge from multiple readings.
7. Students can formulate and communicate constructive critiques in scholarly contexts.
8. Students can articulate the strengths and weaknesses of selected research methods.
|
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0004 Advanced Topics in Artificial Intelligence (AI) (L+E)
Moduldetails: 03SM22MI0004 Advanced Topics in Artificial Intelligence (AI) (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Artificial Intelligence (AI) techniques have become ubiquitous: we all repeatedly interact with artefacts that are driven by some AI technology.
The goal of this course is to go beyond the foundational approaches (which are covered in other classes) and explore advanced topics in artificial intelligence such as large-scale knowledge processing, encoding mechanisms for unstructured and structured data, the combination of various AI approaches to provide intelligent systems, as well as notions of human-AI collaboration and coordination spanning from collective intelligence to ensuring notions of fairness, accountability, transparency and diversity. |
| Lernziel |
The students can
- apply the theory and practice of advanced AI methods to realistic scenarios
- develop novel AI methods and applications based on existing cutting-edge research
- reason about the impact of AI applications on society and devise methods that exploit the technology's advantages whilst mitigating the risks |
| Unterrichtssprache |
English |
| Voraussetzungen |
This course assumes that you have taken an introductory AI course such as Introduction to Artificial Intelligence (offered in the spring term) or the previously offered Practical AI.
This is roughly equivalent to covering Chapters 1 - 18 in the Book Artificial Intelligence: A Modern Approach 4th Ed. |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0005 Foundations of Data Science (L+E)
Moduldetails: 03SM22MI0005 Foundations of Data Science (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Introduction to data science and different paradigms of machine learning; Linear prediction, Regression; Maximum Likelihood; Regularization, Generalization, Cross Validation; Optimization; Logistic Regression; Generative Models for Classification, Gaussian Discriminative Analysis, Naïve Bayes; Support Vector Machines; Kernel Methods; Neural Networks, Backpropagation; Clustering; Dimensionality Reduction, PCA. |
| Lernziel |
This course introduces supervised and unsupervised learning. Students will learn the algorithms that underpin popular machine learning techniques. They will also develop an understanding of the theoretical relationships between these algorithms. The practicals will concern the implementation of machine learning algorithms and applications of machine learning. |
| Unterrichtssprache |
English |
| Voraussetzungen |
Introductory courses on continuous mathematics, linear algebra, probability theory, such as: WWF courses Mathematics I, Mathematics II, and Statistics. |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0010 Protocols for Multi-media Communications (PMMK) (L+E)
Moduldetails: 03SM22MI0010 Protocols for Multi-media Communications (PMMK) (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Based on the basic course on communication networks, this PMMK lecture will deepen concepts and principles of efficient networking, advanced communication protocols, data formats and procedures, and their respective Quality-of-Service (QoS) management. It does address the basics in high-speed networks and optical networks, including Passive Optical Networks (PON) being complemented by ADSL, the IP Technology, and MPLS.
It is important to note that the PMMK lecture does address an integrated viewpoint of a full communication system, which does outline dependencies of networks, protocols, QoS and network or protocol architectures, especially by addressing QoS basics and its modeling, QoS methods, and QoS monitoring.
Additionally, protocols for an operations management, multimedia transport protocols (RTP, SCTP), messaging and overlays (VPN, CDN), optimized transport for multimedia and real-time streaming, and Software-defined Networks (SDN) are introduced and discussed. |
| Lernziel |
Students will receive a deep insight into protocols for multi-media communications, Quality-of-Service (QoS) models, and supporting network technologies. More specifically, the lecture will teach up-to-date knowledge in networks, covering available technology and research. This will enable students to develop on their own at a later stage efficient, performing, and globally applicable multi-media communications. Those protocols and mechanisms taught may leave the grounds of typical text books, which provides hooks to students to see in which way research in that field is undertaken. The finalizing discussion of technologies and QoS-based services offered takes into account where applicable economic incentives, which may limit or encourage the use of a dedicated communication service. |
| Unterrichtssprache |
English |
| Voraussetzungen |
The lecture "Computer Networks and Distributed Systems (CNDS)" is recommended highly, but formally not mandatory, in case of a personal dedication to get hold of those basics on the students' own will. |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0011 Object-Oriented Software Development (V) (Objektorientierte Softwareentwicklung)
Moduldetails: 03SM22MI0011 Object-Oriented Software Development (V) (Objektorientierte Softwareentwicklung)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
3 |
| Lehrform |
Vorlesung |
| Allgemeine Beschreibung |
The lecture provides a comprehensive overview of object-oriented software development. After a short introduction to the basics of object-orientation, the focus is on class libraries, design patterns, and frameworks. Primarily Java and Java-based frameworks such as Spring are used for illustration. |
| Lernziel |
The participants know the core concepts of object-oriented software development. They can apply common design patterns to typical problems.
Furthermore, they are able to evaluate a simple software design, suggest improvements and also implement them. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0012 Enterprise IT-Architectures (L+E)
Moduldetails: 03SM22MI0012 Enterprise IT-Architectures (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
3 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
The course addresses Enterprise IT Architectures in a digital world by providing an introduction to the work of an architect in the IT industry. Architectural Methods are used to structure, describe, and specify solutions, and to define scope and context as well as logical and runtime architectures based on functional and non-functional requirements. Work products, documents, diagrams and models are discussed and used in order to specify and communicate the architecture of a solution. The students will work in a team on a proposal of a real case and will present their case studies in one of the lectures. In addition, current important technology concepts like cloud computing, web security, SOA (Service Oriented Architecture) and BPM (Business Process Management) will be introduced. Finally, Enterprise Architecture concepts and Governance of architectures within a complex organizational structure of a company will be discussed. |
| Lernziel |
Students learn how architects work and which basic work products are used to specify architectures. |
| Unterrichtssprache |
English |
| Voraussetzungen |
Basis in Software Engineering and Modeling |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0014 Human Aspects of Software Engineering (L+E)
Moduldetails: 03SM22MI0014 Human Aspects of Software Engineering (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Producing great software as fast as the market demands requires great, productive developers. Yet, what does it mean for an individual developer to be productive, and how can we best help developers to be productive? To answer these questions, researchers in software engineering have been, and are still predominantly looking at the output that software developers create, such as the applications, the source code, or the test cases. This output-oriented focus misses one of the most essential parts in the process of software development: the individual developer who creates the software.
Recent advances in technology afford the opportunity to collect a wide variety of detailed information on a software developer and her work, ranging from the number of resolved work items all the way to the cognitive load the developer experiences while working. The availability and accessibility of data on each developer is enabling us to explore questions about developer productivity in powerful new ways.
In this course, we investigate how we can ensure the human ingenuity and smarts are being amplified by the processes and tools used to create systems, rather than the humans spending precious cognitive effort dealing with mundane or unnecessary problems.
This is an overview of the kinds of topics we cover in this course:
* quantitative and qualitative evaluation of software engineering research
* (biometric) sensing in software development
* developer retrospection, productivity, and well-being
* work fragmentation and interruptions
* code navigation and exploration
* program comprehension
* software development tools and environments. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0019 Network Science (L+E)
Moduldetails: 03SM22MI0019 Network Science (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Network Science is an interdisciplinary field of research that has become synonym with the study of multiple complex systems that pervade social and economic systems. Network refer to representations of systems whose constituents are linked together because of social ties, information flow, economic relations, etc. Network modelling is a methodology with ample applications in modern data-intensive fields which has multiple applications in management, marketing, informatics, among multiple others.
The course covers topics a wide range of topics: it starts with an introduction to the basic concepts about networks; it then deals with the most important properties that real-world networks exhibit, and how they can be modelled; then, it introduces network analytic techniques to uncover the most important properties of empirical networks. Finally, an introduction to the diffusion of technologies, opinions and rumours (and viruses!) are taught.
During the course, special emphasis is employed in introducing network analysis and visualisation tools.The course is highly interactive. All the lectures consist of a theoretical part, then, the students must develop (in small groups and always supported by the instructors) the some practical exercises themselves. This permits them to gain direct experience and familiarity with the concepts taught and the techniques involved. In this participatory environment, multiple exercises and the creation of visualisations play an important role. |
| Lernziel |
At the end of the course students:
- are able to construct network representations of complex datasets
- characterise and understand topological properties of networks
- know the typical characteristics of networks in social, economic and technology systems
- can understand mechanisms that lead to the emergence of large scale network properties |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0023 Randomized Algorithms (L)
Moduldetails: 03SM22MI0023 Randomized Algorithms (L)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung |
| Allgemeine Beschreibung |
This lecture covers several aspects of randomness in computation. Firstly, we establish basic probablistic tools, such as linearity of expectation and bounds on probabilities. Building on these we cover the design and analysis of randomized algorithms. Such an algorithm is allowed to use "coin flips" in its decision making. Some randomized algorithms yield significantly better runtimes and/or solution qualities as their deterministic counterparts. We also treat stochastic processes, e.g. Markov chains in the lecture, and give classical applications like 3-SAT. More advanced topics like random graphs, the probabilistic method, and randomized rounding are also covered.
Mathematically sound analysis is an important and integral part of this lecture. That is, we will not only state the properties of an algorithm, e.g.
its correctness or running time, but also prove them mathematically.
In particular, we plan to treat the following topics:
Introduction
Linearity of Expectation: Concept, Balls-Into-Bins, Coupon Collector, Quicksort
Bounds on Probabilities: Markov, Chebyshev, Chernoff, Balls-Into-Bins, Coupon Collector, Quicksort
Markov Chains: Concept, Hitting Times and Probabilities, Random Walks, Invariant Distributions, 3-SAT
Randomized Rounding: Concept, SET COVER, MAX SAT, Derandomization
Probabilistic Method: First- and Second Moment Method, MAX SAT, Random Graphs |
| Lernziel |
The goal is to learn about the most important algorithmic design principles and techniques for their analysis related to randomness in computation. |
| Unterrichtssprache |
English |
| Voraussetzungen |
The content of the following lectures are mandatory prerequisites: 1.
Informatics I, 2. Informatics II, 3. Foundations of Computing I |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes 2. Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SM22MI0043 Reinforcement Learning (L+E)
Moduldetails: 03SM22MI0043 Reinforcement Learning (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
This class offers a comprehensive introduction to the field of Reinforcement Learning (RL). Students will explore the core challenges and various approaches within RL. By the end of the course, students will be well-versed in the key concepts and techniques essential for mastering Reinforcement Learning. |
| Lernziel |
By the end of the course, students will understand the key concepts of Reinforcement Learning (RL). Specifically, students will be able to:
- Define the key features of RL that distinguishes it from the other fields in Machine Learning
- Implement common RL algorithms in code.
- Describe various criteria for analyzing RL algorithms and evaluate them based on metrics such as regret, sample complexity, computational complexity, empirical performance, and convergence, as assessed through assignments.
- Formulate and solve sequential decision-making problems using relevant RL tools. |
| Unterrichtssprache |
English |
| Voraussetzungen |
- Proficiency in Python
- Calculus, Linear Algebra: you should be comfortable taking derivatives and understanding matrix vector operations and notation.
- Basic Probability and Statistics: You should know basics of probabilities, Gaussian distributions, mean, standard deviation, etc.
- Foundations of Machine Learning
|
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
03SMDINF2039 Vision Algorithms for Mobile Robotics (L+E)
Moduldetails: 03SMDINF2039 Vision Algorithms for Mobile Robotics (L+E)
| Modulgruppe |
Computer Science |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Vorlesung mit integrierter Übung |
| Allgemeine Beschreibung |
Have you ever been curious to learn the perception algorithms used in today's self-driving cars and drones and what they have in common with virtual and augmented reality? Then this course is for you! This course introduces you to the key algorithms behind Apple ARKit, Google Visual Positioning Service, Microsoft Hololens, Magic Leap, Oculus Quest, Oculus Insight, and the NASA Mars rovers. In particular, we course will cover these topics: image formation, filtering, feature extraction, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, visualinertial odometry, and deep learning. Each lecture will be followed by a lab session where you will learn to implement the building block of a visual odometry algorithm in Matlab. By the end of the course, you will integrate all these building blocks into a working visual odometry algorithm. |
| Lernziel |
By the end of the course you will know how to implement the fundamental computer vision algorithms used in mobile robotics, in particular: image formation, filtering, feature extraction, multiple view geometry, dense reconstruction, object tracking, image retrieval, event-based vision, visual-inertial odometry, and deep learning. |
| Unterrichtssprache |
English |
| Voraussetzungen |
keine |
| Leistungsnachweis |
Siehe Vorlesungsverzeichnis |
| Notenskala |
1-6, in Viertelschritten |
| Repetierbarkeit |
einmal wiederholbar |
| Angebotsmuster |
1-semestrig (jedes Herbstsemester) |
| Organisation |
Wirtschaftswissenschaftliche Fakultät |
06SM523-510 Practical Training In-House
Moduldetails: 06SM523-510 Practical Training In-House
| Modulgruppe |
Computational Linguistics and Language Technology in Practice |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Praktikum |
| Allgemeine Beschreibung |
In this module, the students get in touch with scientific project work, that is, they learn how to do basic research. In order to accomplish these kind of skills, they read scientific literature, prepare and annotate data, apply statistical and machine learning methods to solve particular problems. They are also involved in the preparation of articles for workshops and conferences. The students work on a particular (partial) problem in a scientic context or even running project. This module can be booked to credit work done in a scientific project at the UZH. This module can be booked with 6 or 9 ECTS points. The amount of points will be decided in consultation with the module coordinator. |
| Lernziel |
The students (1) get in touch with research (2) read scientific literature (3) are involved in evaluation processes (4) take over particular tasks in the context of a project (5) are involved in the preparation of articles (6) get insights into practical work (7) deepen their knowledge and skills with respect to a particular topic |
| Unterrichtssprache |
Deutsch und/oder Englisch |
| Voraussetzungen |
This module cannot be booked by the students themselves, the booking has to be authorized by the module coordinator. There is no entitlement to this module, the module will only be offered if a suitable position is available in a project. The requirements will be defined according to the topic. |
| Leistungsnachweis |
documented practical work |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Semester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-511 Practical Training Off-Site
Moduldetails: 06SM523-511 Practical Training Off-Site
| Modulgruppe |
Computational Linguistics and Language Technology in Practice |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Praktikum |
| Allgemeine Beschreibung |
The students gain experience in the application of computational linguistics. They get in touch with the structures and procedures of companies and are involved into the realisation of software in order to solve particular problems of these companies. The students apply what they have learned and adapt it to the needs of a specific commercial sector. Practical Trainings Off-Site are usually stays at companies or public organizations that are involved with Natural Language Processing. The training has to have a relation to Natural Language Processing and they have to be organized autonomously. This module can be booked with 3 or 6 ECTS points. The amount of points will be decided in consultation with the module coordinator. |
| Lernziel |
The students (1) get in touch with language technology companies (2) learn to connect theory and practical work (3) get to know the structures and processes of companies (4) apply what they have learned (5) broaden their knowledge of practical issues |
| Unterrichtssprache |
Deutsch und/oder Englisch |
| Voraussetzungen |
This module cannot be booked by the students themselves, the booking has to be authorized by the module coordinator. A prior application must be approved by the module coordinator in order for the Practical Training Off-Site to be credited.
This module is open only to Master's students. It may not be booked by Bachelor's students as a pre-Master's module. |
| Leistungsnachweis |
documented practical work |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Semester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-512 Programming Project 1
Moduldetails: 06SM523-512 Programming Project 1
| Modulgruppe |
Computational Linguistics and Language Technology in Practice |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Sonstiges |
| Allgemeine Beschreibung |
Programming projects aim at the consolidation of programming and the acquisition of software engineering skills. Starting with a particular research question and relevant literature, they work on a solution, define milesstones, aquire and/or annotate data, implent a programm and evaluate it using appropriate data. This module can be booked to credit work done in a programming project. This module can be booked with 3, 6 or 9 ECTS points. The amount of points will be decided in consultation with the module coordinator. |
| Lernziel |
The students (1) autonomously design a project (2) realize the project plan (3) use existing tools (4) do software engineering (5) document their work according to standards (6) evaluate the results (7) use software repositories |
| Unterrichtssprache |
Deutsch und/oder Englisch |
| Voraussetzungen |
In the duration of a study level a maximum of two programming projects can be booked. This module can be booked to credit work done in a programming project. It cannot be booked by the students themselves, the booking has to be authorized by the module coordinator. Before a programming project is started, it is essential to get the permission of the module coordinator (per Email). The prerequisites will be set according to the topic. |
| Leistungsnachweis |
documented practical work |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Semester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-513 Student Teaching Assistant 1
Moduldetails: 06SM523-513 Student Teaching Assistant 1
| Modulgruppe |
Computational Linguistics and Language Technology in Practice |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Sonstiges |
| Allgemeine Beschreibung |
A student teaching assistance serves the acquisition of basic teaching skills. This requires a deeper insight of the contents of the associated lecture and the ability to prepare teaching material in order to help the students to better understand it. The task also involves the preparation and correction of exercises. This module can be booked to credit the conducting of exercises/tutorials. This module can be booked with 3 or 6 ECTS points. The amount of points will be decided in consultation with the module coordinator. |
| Lernziel |
The students (1) cope with computational linguistics content from a teaching perspective (2) learn to prepare computational linguistics content in a way tailored to a student's audience (3) learn to correct exercises and give appropriate feedback |
| Unterrichtssprache |
Deutsch und/oder Englisch |
| Voraussetzungen |
In the duration of a study level a maximum of two modules «Student Teaching Assistant» can be booked, whereby the two modules must differ in content (also to any previously completed student teaching assistant modules). This module is booked in order to receive credit for a first job as a student teaching assistant at master's level.
This module is an application module, the application has to be authorized by the module coordinator (per Email). The lecturers have to be included in the communication. The open positions for student teaching assistants are usually posted on the mailing list of the Institute of Computational Linguistics (cllist@ lists.ifi.uzh.ch) a few weeks before the semester starts. Students interested in conducting exercises/tutorials of a specific course can apply anytime for the position directly with the lecturer and the module coordinator. The module in question must have been passed successfully beforehand. |
| Leistungsnachweis |
documented practical work |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Semester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-516 Student Teaching Assistant 2
Moduldetails: 06SM523-516 Student Teaching Assistant 2
| Modulgruppe |
Computational Linguistics and Language Technology in Practice |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Sonstiges |
| Allgemeine Beschreibung |
A student teaching assistance serves the acquisition of basic teaching skills. This requires a deeper insight of the contents of the associated lecture and the ability to prepare teaching material in order to help the students to better understand it. The task also involves the preparation and correction of exercises. This module can be booked to credit the conducting of exercises/tutorials. This module can be booked with 3 or 6 ECTS points. The amount of points will be decided in consultation with the module coordinator. |
| Lernziel |
The students: - cope with computational linguistics content from a teaching perspective; - learn to prepare computational linguistics content in a way tailored to a student's audience; - learn to correct excercises and give appropriate feedback. |
| Unterrichtssprache |
Deutsch und/oder Englisch |
| Voraussetzungen |
In the duration of a study level a maximum of two modules «Student Teaching Assistant» can be booked, whereby the two modules must differ in content (also to any previously completed student teaching assistant modules). This module is booked in order to receive credit for a second job as a student teaching assistant at master's level.
This module is an application module, the application has to be authorized by the module coordinator (per Email). The lecturers have to be included in the communication. The open positions for student teaching assistants are usually posted on the mailing list of the Institute of Computational Linguistics (cllist@ lists.ifi.uzh.ch) a few weeks before the semester starts. Students interested in conducting exercises/tutorials of a specific course can apply anytime for the position directly with the lecturer and the module coordinator. The module in question must have been passed successfully beforehand. |
| Leistungsnachweis |
documented practical work |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Semester) |
| Organisation |
Institut für Computerlinguistik |
06SM523-517 Programming Project 2
Moduldetails: 06SM523-517 Programming Project 2
| Modulgruppe |
Computational Linguistics and Language Technology in Practice |
| Modultyp |
Wahlpflicht |
| ECTS |
6 |
| Lehrform |
Sonstiges |
| Allgemeine Beschreibung |
Programming projects aim at the consolidation of programming skills and the acquisition of software engineering skills. Starting with a particular research question and relevant literature, they work on a solution, define milestones, acquire and/or annotate data, implement a program and evaluate it using appropriate data. This module can be booked with 3, 6 or 9 ECTS points. The amount of points will be decided in consultation with the module coordinator. |
| Lernziel |
The students: - autonomously design a project; - realise the project plan; - use existing tools; - do software engineering; - documuent their work according to standards; - evalute the results; - use software repositories. |
| Unterrichtssprache |
Deutsch und/oder Englisch |
| Voraussetzungen |
In the duration of a study level a maximum of two programming projects can be booked. This module can be booked to credit work done in a second programming project. It cannot be booked by the students themselves, the booking has to be authorized by the module coordinator. Before a programming project is started, it is essential to get the permission of the module coordinator (per Email). The prerequisites will be set according to the topic. |
| Leistungsnachweis |
documented practical work |
| Notenskala |
bestanden/nicht bestanden |
| Repetierbarkeit |
einmal wiederholbar, erneut buchen |
| Angebotsmuster |
1-semestrig (jedes Semester) |
| Organisation |
Institut für Computerlinguistik |
Ende
