Artificial intelligence is undisputedly one of the most important future technologies on which the global competitiveness of the German economy will decisively depend. This is where the Master's programme "Applied Artificial Intelligence and Digital Transformation" (KDT) comes in: The aim is to train experts for operational applications of artificial intelligence that can be used directly in companies and mainly support successful implementation and digital transformation. The focus will be on the constructive development of innovative, intelligent and at the same time economically feasible solutions, especially in the areas of production, marketing and human resources.
Therefore, interdisciplinary trained experts* are necessary, who, in addition to the urgently needed mathematical-technical expertise, e.g. on symbolic (rule and decision tree induction) and sub-symbolic (neural networks, deep learning) methods of machine learning, also possess competences for the organisational implementation of the requirements under consideration of the economic, legal and ethical framework ("Business Understanding"). Their task will be to develop solutions together with the respective experts (mathematicians, IT representatives, engineers, management representatives) and to lead or accompany the implementation within the framework of change projects. The focus is therefore on a broad education that guarantees the learning of fundamental aspects and especially the technical language of the relevant areas.
Short form | KDT |
Type of study | Full time |
Standard period of study | 3 semester |
Closing | Master of Arts (M.A.) |
Start of studies | Winter semester |
Admission restrictions | specific |
Lecture location | Ansbach |
Language of instruction | German |
Course management | Prof. Dr. Sigurd Schacht |
Student advisory service | Prof. Dr. Sigurd Schacht |
Study programme assistance | studiengangsassistenz-kdt(at)hs-ansbach.de |
Student services | studierendenservice.kdt(at)hs-ansbach.de |
You can start the KDT Master's programme in the winter semester. As for all degree programs, you must apply for this program in good time via the online application portal. Please note that the regular deadlines are cut-off deadlines. Your application must therefore be received by us at the latest by the end of the last day of the respective deadline (May 2 - July 15).
You can find all information on how to applyHERE.
In addition, a successfully completed university degree in a relevant field of study or an equivalent domestic or foreign degree with an overall examination grade of at least 2.5, the scope of which usually comprises 210 ECTS points, but at least 180 ECTS points, is required. Courses of study are considered relevant if they are based on fundamentals from the fields of media, economics or technology.
The Master's programme "Applied Artificial Intelligence and Digital Transformation" comprises 90 ECTS, which can be completed in three semesters. If you start with a degree of less than 210 ECTS, you may have to plan additional time to catch up on modules/ECTS.
The programme is strongly practice-oriented and offers in the first semester, in addition to a basic technological education, early specialisationpossibilities within the modules in the areas of digital business models and processes as well as leadership and change management. In order to optimally prepare students for their future professional life, the teaching content from the various lectures is applied directly in practice-oriented group exercises starting in the first semester. In the 2nd semester, the teaching content is put into practice in an independent applied project, usually with an external partner. Accompanying this, operational application possibilities of the AI are offered in three different areas. In addition, these modules prepare the graduates for their future professional life by providing in-depth practical examples. After the project work, the 3rd semester offers the possibility to deepen the knowledge (e.g. project work) on the basis of a master thesis.
You will complete your studies in three semesters. Upon successful completion you will be awarded the internationally recognized academic degree Master of Arts (M.A.).
The combination of basic knowledge about applied AI, knowledge about the digitalization of products and processes, the profound knowledge in the field of digital leadership and transformation enables students to have a diverse and above all industry-independent professional future perspective. Whether in the free economy, in non-commercial organisations or public authorities - the later spectrum of activities is broad.
On the one hand, the interdisciplinary know-how imparted enables the graduates to independently recognize cross-project potentials for AI solutions and to implement these in turn through structured and goal-oriented action. On the other hand, in the field of applied AI and digital transformation, they are qualified to take on management responsibility and to initiate and implement AI projects and to subsequently subject them to extensive evaluation with regard to quality assurance.
Within this framework, the graduates are not only interesting for global corporations or large authorities, but also for small and medium-sized enterprises (SME) due to the comprehensive range of applications.
Studiengangsleiter Angewandte Künstliche Intelligenz und Digitale Transformation (KDT) / Studienfachberatung Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Studiengangsleiter Angewandte Künstliche Intelligenz und Digitale Transformation (KDT) / Studienfachberatung Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Sigurd Schachts Lehre und Forschung ist fokussiert auf die Anwendung der Verfahren der künstlichen Intelligenz in Unternehmen und Gesellschaft. Vor seiner Tätigkeit an der HS Ansbach, war er Professor an der HS Heilbronn und langjährig bei zwei der BIG-4-Prüfungsgesellschaften tätig.
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Studiengangsassistenz Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Studiengangsassistenz Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Leiter Studierendenservice der School of Business and Technology (SBT)
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Leiter Studierendenservice der School of Business and Technology (SBT)
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Professor Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Professor Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Professor Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
Professor Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Publikationen:
Müller, Michael: Wissensmanagement. Reihe „WiWi klipp & klar“. Springer Gabler, Wiesbaden, 2022.
Förtsch, Ferdinand; Müller, Michael: Wissensmanagement – Karriere in der Verwaltung. Kommunal- und Schul-Verlag, Wiesbaden, 2015.
Ittner, Friedrich-Alexander; Ambrosius, Ute; Müller, Michael: Wissensorientierte Strategieentwicklung für den Mittelstand (KMU). Schriftenreihe Campus Edition, Fachhochschule Ansbach, 2015.
Kaiser, Robert; Müller, Michael: Erfahrungsschätze bewahren. In Vitako aktuell, Zeitschrift der Bundes-Arbeitsgemeinschaft der Kommunalen IT-Dienstleister e.V., 2010.
Punzel, Joachim; Schwarze, Jochen; Graubner, Christian; Müller, Michael: Fallstudie: Von der Mindmap zum Wiki - Wissensbewahrung im eGovernment-Center der Stadt Erlangen. In „Enterprise 2.0 - Planung, Einführung und erfolgreicher Einsatz von Social Software in Unternehmen“, Forschungsgruppe Kooperationssysteme der Universität der Bundeswehr München, 2009.
Graubner, Christian; Müller, Michael: Wissensmanagement für Innovationsmanagement - Systematische Unterstützung des Innovationsmanagements durch Methoden und Prozesse des Wissensmanagements. In Tagungsband WM 2007 (4. Konferenz Professionelles Wissensmanagement), 2007.
Müller, Michael; Kaiser, Robert: Wissensbewahrung bei der Stadt Erlangen - Dokumentation und Kommunikation der Erfahrungen ausscheidender Wissensträger. In Tagungsband KnowTech 2006, S. 425-432, 2006.
Stoyan, Herbert; Müller, Michael; Bimazubute, Raymond; Grille, Barbara; Hausdorf, Carsten; Hormeß, Markus; Kraetzschmar, Gerhard K.; Schneeberger, Josef; Turk, Andreas: Wissenserwerb und Wissensmanagement. In Hammwöhner, R.; Rittberger, M.; Semar, W. (Hrsg.): Wissen in Aktion - Der Primat der Pragmatik als Motto der Konstanzer Informationswissenschaft, Festschrift für Rainer Kuhlen, Schriften zur Informationswissenschaft 41, Hochschulverband für Informationswissenschaft, S. 253-270, 2004.
Grille, Barbara; Stoyan, Herbert; Gaede, Bernd; Müller, Michael: Wissensmanagementprozesse - Mit Experteninterviews zum aufgabenbezogenen hypertextbasierten Informations- und Tutorsystem. In Buhl, H.-U.; Huther, A.; Reitwiesner, B. (Hrsg.): 5. Internationale Tagung Wirtschaftsinformatik 2001, S. 323-338, Physica-Verlag, 2001.
Hausdorf, Carsten; Stoyan, Herbert; Müller, Michael: Kaskadierter Wissenserwerb beim Wissensmanagement. In Gronau, N. (Hrsg.): Wissensmanagement: Systeme - Anwendungen - Technologien, Aachen, 2001.
Hogl, Oliver; Müller, Michael; Stoyan, Herbert; Stühlinger, Wolf: On Supporting Medical Quality with Intelligent Data Mining. In Sprague, R. (Hrsg.): Proceedings of the Thirty-Fourth Annual Hawaii International Conference on System Sciences (HICSS-01), IEEE Press, Maui, Hawaii, January 3-6 2001.
Hogl, Oliver; Stoyan, Herbert; Müller, Michael: The Knowledge Discovery Assistant: Making Data Mining Available for Business Users. In Gunopulos, D.; Rastogi, R. (Hrsg.): Proceedings of the 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD-2000), S. 96-105, Dallas, Texas, May 2000.
Müller, Michael: Interessantheit bei der Entdeckung von Wissen in Datenbanken. In Künstliche Intelligenz, Nr. 3, S. 40-42, September 1999.
Müller, Michael: Interessantheit bei der Entdeckung von Wissen in Datenbanken. Dissertation, Friedrich-Alexander-Universität Erlangen-Nürnberg, 1998.
Müller, Michael; Hausdorf, Carsten; Schneeberger, Josef: Zur Interessantheit bei der Entdeckung von Wissen in Datenbanken. In Nakhaeizadeh, Gholamreza (Hrsg.): Data Mining: Theoretische Aspekte und Anwendungen, Beiträge zur Wirtschaftsinformatik, S. 248-264, Physica-Verlag, Heidelberg, 1998.
Büchter, Oliver; Müller, Michael; Schneeberger, Josef: Improving Forward Selection with Background Knowledge: Finding Interesting Multiple Regression Models for Sales Prediction. In Lu, Hongjun; Motoda, Hiroshi; Liu, Huan (Hrsg.): Proceedings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’97), KDD: Techniques and Applications, S. 344-357, World Scientific, Singapore, 1997.
Professor Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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Professor Angewandte Künstliche Intelligenz und Digitale Transformation (KDT)
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