Difference between revisions of "IAS Modules"
Line 41: | Line 41: | ||
==== [InfM-ML, 9 CP] Machine Learning (ML) ==== | ==== [InfM-ML, 9 CP] Machine Learning (ML) ==== | ||
− | * [http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/teaching/2013-ss-vorlesung-ml/index.shtml Lecture: Algorithmic Learning (Algorithmisches Lernen)] | + | * ???????''[http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/teaching/2013-ss-vorlesung-ml/index.shtml Lecture: Algorithmic Learning (Algorithmisches Lernen)]''???? |
* [http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/teaching/2014-ss-vorlesung-ml/index.shtml Machine Learning (Maschinelles Lernen)] | * [http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/teaching/2014-ss-vorlesung-ml/index.shtml Machine Learning (Maschinelles Lernen)] | ||
Revision as of 16:13, 27 August 2019
As one of the few computer science programs in Germany that are taught completely in English, the Master course „Intelligent Adaptive Systems“ prepares students for international research and professional work.
Following a balanced approach, it is placed in between programs with a purely technical, low-level hardware focus and programs that offer a general computer science curriculum. Its curriculum is focused on the area of smart technology and intelligent adaptive systems and provides in-depth training in this area. The selected modules provide a comprehensive overview including technical aspects and state-of-the art algorithms and methods. Students are introduced to current research in the corresponding fields and have the opportunity to deepen the acquired knowledge by participating in international research projects.
The Master in Intelligent Adaptive Systems is a 2-year research oriented programme that is taught in English. Students, both national and international, can profit from an international environment and improve their grasp of the English language and knowledge on cultural differences. The inter-student exchange is fostered in seminars and work groups and extended in extra-curricular discussions and activities.Through the proximity to current research projects, students have the possibility of a smooth transition into collaborative research environments and continuing education and study.
Contents
IAS Curriculum
The Master’s programme is comprised of 120 credit points that are distributed between compulsory core lectures, selectable focus and extending lectures, and project work:
- Core Modules (39 CP)
- Focus Choice (24 CP)
- Extending Choice (15 CP)
- Project and Thesis (42 CP)
As a general rule, 1 ECTS credit corresponds to 25–30 hours of work.
Core Modules = 39 CP
Core lectures are compulsory for all students. This set of lectures conveys an in-depth understanding of different types of intelligent adaptive systems and introduces students to the most current research in the different areas. All core modules consist of a combination of lecture and seminar/tutorial to foster student participation and constant application of learned concepts. In the following sections, you can find direct links to the internal websites of the modules with information on content and organisation.
[InfM-SWA, 6 CP] Software Architecture (SA)
- Lecture: Software Architecture (Softwarearchitektur)
- Integrated Seminar: Architecture-centric Software Development (Architekturzentrierte Softwareentwicklung)
[InfM-BAI, 6 CP] Bio-Inspired Artificial Intelligence (BAI)
- Lecture: Bio-Inspired Artificial Intelligence (Bioinspirierte Künstliche Intelligenz)
- Integrated Seminar: Bio-Inspired Artificial Intelligence (Bioinspirierte Künstliche Intelligenz)
[InfM-IR, 6 CP] Intelligent Robotics (IR)
- Lecture: Intelligent Robotics (Intelligente Roboter)
- Seminar: Intelligent Robotics (Intelligente Roboter)
[InfM-NN, 6 CP] Neural Networks (NN)
- Lecture: Knowledge Processing with Neural Networks (Wissensverarbeitung mit Neuronalen Netzwerken)
- Integrated Seminar: Knowledge Processing with Neural Networks (Wissensverarbeitung mit Neuronalen Netzwerken)
[InfM-ML, 9 CP] Machine Learning (ML)
- ???????Lecture: Algorithmic Learning (Algorithmisches Lernen)????
- Machine Learning (Maschinelles Lernen)
[InfM-RM, 6 CP] Research Methods (RM)
- Lecture: Research Methods (Wissenschaftliches Arbeiten)
- Practical: Research Methods (Wissenschaftliches Arbeiten)
Focus Choice = 18 CP (+6CP students from 2018)
Focus choice slots provide students with the opportunity to strengthen their background in a chosen field or deepen their knowledge in a field which complements the core modules. Focus modules will be chosen in consultation with an assigned advisor and can be selected from a list that was put together to align well with the overall focus of the master. This list will contain single modules which supplement core lectures as well as suggested sets of lectures that together form a coherent focus area. It will be reviewed on a regular basis to reflect current research and to include newly emerged and complementary teaching areas. The following list shows the modules currently available for focus option slots in the winter or summer semester.
Winter Semester
Knowledge Processing (WV1)
- Lecture: Knowledge Representation (Wissensrepräsentation)
- Integrated Seminar: Knowledge Representation (Wissensrepräsentation)
Image Processing (BV1)
Evaluation of Computer Networks (LTR)
- Limited information available in English Lecture: Evaluation of Computer Networks (Leistungs-/Zuverlässigkeitsbewertung und Traffic-Engineering für Rechnernetze)
- Limited information available in English Integrated Seminar: Evaluation of Computer Networks (Leistungs-/Zuverlässigkeitsbewertung und Traffic-Engineering für Rechnernetze)
Summer Semester
Language Processing (SV)
- Lecture: Language and Speech in Multimodal Interaction (Sprachverarbeitung)
- Integrated Seminar: Dialogue Systems (Sprachverarbeitung)
Image Processing (BV2)
- No information available yet in English Lecture: Image Processing by Remote Sensing (Bildverarbeitung II - Vorlesung Bildverarbeitung in der Fernerkundung)
- No information available yet in English Lecture: Image Processing - Intermediate Methods and Applications of Image Processing (Bildverarbeitung II - Fortgeschrittene Methoden und Anwendungen der Bildverarbeitung)
Robot Technology (RT)
- Lecture: Introduction to Robotics
- Practical: Robot Practical Course. Material will only be announced in the lectures.
Mobile Networks and Communication (MNE)
- Lecture: Mobile Networks and Communication (Mobilnetze, dienstintegrierte Netze und Echtzeitkommunikation) Currently no information available in English
- Tutorial: Mobile Networks and Communication (Mobilnetze, dienstintegrierte Netze und Echtzeitkommunikation) Currently no information available in English
Extension Choice = 12CP
12 credit points can be selected from a range of lectures taught at the Department of Informatics (List of all Infomartic courses taught on English) or other departments. In comparison to the focus options, these modules can be used to gain knowledge in fields that go beyond the scope of this programme, but are linked to its contents, e.g. Psychology or Biology. The lectures are again chosen in consultation with an advisor, to guarantee a meaningful choice in alignment with the student’s background and aims.
Project = 12 CP and Master Module (Thesis + Seminar) = 30 CP
After lectures and seminars, where the focus is usually on individual work, the student has to participate in a group project, before undertaking a research project that finally leads to the Master’s thesis. In the group project, the focus is on teamwork and a scientific exchange and defense of ideas, to prepare students for a collaborative scientific environment. Students are encouraged to choose projects in preparation of their research project and to actively take part in research projects of a chosen area. Two to three students are expected to work as an independent group with a supervisor from the corresponding area.
A seminar, where all groups meet, gives students the opportunity to present their work to a wider audience of peers, in an environment comparable to a scientific conference. In the last term, students work full time on an independent research project which ends with submission of the final Master’s thesis.