20 h Gesamtstunden: 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] Nachbereitung: ca. 70 h Leistungsnachweise: ca. 20 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] 30h Gesamtstunden: 150h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

machine learning. • Methodological competence: The students are able to practically apply various machine learning methods and to evaluate the results. • Personal competence: Ability to discuss [...] Personal competence: Ability to communicate about lightweight engineering; ability to work independently as well as in team to solve a technical problem; ability to lifetime learning Course Content Inhalte [...] private situations. They learn to identify these situations and to appear interculturally competent. • Personal competence: Students acquire the interdisciplinary ability to perform in a culturally

Machine Learning mit Scikit-Learn, Keras und TensorFlow“, O'Reilly; 2. Edition (2020) Bishop, C.M.: „Pattern Recognition and Machine Learning“, Springer (2006) Chollet, F.: „Deep Learning with Python“ [...] Seite 102 von 116 6.6.3 Machine Learning for Engineers – Einführung in Methoden und Werkzeuge Machine Learning for Engineers – Introduction to Methods ans Tools Zuordnung zum Curriculum [...] und verschiedener Algorithmen des Machine Learning. • Methodenkompetenz: Die Studierenden sind befähigt, verschiedene Verfahren des Machine Learnings praktisch anzugehen und die Ergebnisse zu

 You will be able to apply electrical measurement technology to practical issues. Methodological competence: You are able to,  apply the instruments and methods learned to case studies and practical [...] introduction to object-oriented programmeming, including an overview of the language syntax and how to develop simple applications. Students will learn how to write custom classes and methods, and how to test [...] Learning Objectives/Competencies to be Assessed Module work (ModA) Project Work in Groups -Details to follow- The group project is used to test the practical learning content and

Machine Learning mit Scikit-Learn, Keras und TensorFlow“, O'Reilly; 2. Edition (2020) Bishop, C.M.: „Pattern Recognition and Machine Learning“, Springer (2006) Chollet, F.: „Deep Learning with Python“ [...] Seite 66 von 85 4.1.3 Machine Learning for Engineers – Einführung in Methoden und Werkzeuge Machine Learning for Engineers – Introduction to Methods ans Tools Zuordnung zum Curriculum [...] verschiedener Algorithmen des Machine Learning. • Methodenkompetenz: Die Studierenden sind befähigt, verschiedene Verfahren des Machine Learnings praktisch anzugehen und die Ergebnisse zu

Machine Learning mit Scikit-Learn, Keras und TensorFlow“, O'Reilly; 2. Edition (2020) Bishop, C.M.: „Pattern Recognition and Machine Learning“, Springer (2006) Chollet, F.: „Deep Learning with Python“ [...] Seite 66 von 86 4.1.3 Machine Learning for Engineers – Einführung in Methoden und Werkzeuge Machine Learning for Engineers – Introduction to Methods ans Tools Zuordnung zum Curriculum [...] verschiedener Algorithmen des Machine Learning. • Methodenkompetenz: Die Studierenden sind befähigt, verschiedene Verfahren des Machine Learnings praktisch anzugehen und die Ergebnisse zu

science and the machine learning domain • Understanding some of the most widely used machine learning methods • Being able to implement a machine learning pipeline in order to solve real world problems [...] Voraussetzungen* Prerequisites This course is an introduction to ML. There is no need to have any prior knowledge about machine learning *Hinweis: Beachten Sie auch die Voraussetzungen nach Prüf [...] limited to linear regression and classification, Support vector machines and Deep neural networks. 3) Introduction to Python programming for data science. 4) Applying machine learning models on

eigenverantwortliches Werken (Projektarbeit unter Nutzung des Hochschul- Lernmanagementsystem meet-to-learn.de) Art der Prüfung (Studienarbeit, Klausur, Leistungsnachweise) schriftliche Prüfung [...] zu durchlaufen. Von der Ideenfindung, Storyboard, der Medienwahl, über Briefings, Pre-production-Meetings, in denen Inhalte und Ideen überprüft werden. Die Diskussion in der Gruppe über den aktuellen [...] Day V, 11. Mai 2007, 3. Lengerich [u.a.], Pabst Science Publ., 2007 4. Bousquet, Michele: How to cheat in 3ds Max 2009, Amsterdam, Focal Press/Elsevier, 2008 5. Wendt, Volker: 3ds Max 9 Workshops

/Pipher, J./Silverman, J. H. (2014): An Introduction to Mathematical Cryptography, 2. Auflage, Springer · Katz, J./Lindell, Y. (2015): Introduction to Modern Cryptography, 2. Auflage, CRC Press · Lipton [...] Synthese gesprochener Sprache (text-to-speech) · Sprachdialogsysteme · Textanalyse, Dokumentanalyse, OCR · Clustering/Klassifikation · Neuronale Netze und Deep Learning Lehrmaterial/Literatur Teaching [...] Vor-/Nachbereitung: 45 h PrA: 45 h Gesamt: 150 h Lernziele/Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

Verfahren (z.B. XAI, Embedded AI, semi-/self-supervised learning, active learning, federated learning, contrastive learning, transfer learning, DL für Audiosignale) Lehrmaterial / Literatur Teaching [...] Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2018. A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras and Tensor Flow, O’Reilly, 2018 Raschka: Machine Learning mit Python: das [...] and Machine Learning, Springer, 2006. F. Chollet: Deep Learning with Python, Manning, 2018. (deutsche Version bei mitp Professional, 2018) A. Géron: Hands-On Machine Learning with Scikit-Learn, Keras, and

Verfahren (z.B. XAI, Embedded AI, semi-/self-supervised learning, active learning, federated learning, contrastive learning, transfer learning, DL für Audiosignale) Lehrmaterial / Literatur Teaching [...] Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2018. A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras and Tensor Flow, O’Reilly, 2018 Raschka: Machine Learning mit Python: das [...] and Machine Learning, Springer, 2006. F. Chollet: Deep Learning with Python, Manning, 2018. (deutsche Version bei mitp Professional, 2018) A. Géron: Hands-On Machine Learning with Scikit-Learn, Keras, and

Lernziele/Kompetenzen Learning outcomes / competences to be assessed portfolio examination Learning portfolio (100%) The Learning Portfolio is used to check the entire learning content and competence [...] achievements of previous studies can only be recognised if the learning outcome can be attributed to a Master level or corresponds to the learning outcome of a module in the OTH Master's degree programme [...] environment. • Personal competence (self-competence): Students learn to self-reflect their cultural values and learn strategies how to assume ethical responsibility in an international context. Inhalte

Expertise - You will learn building blocks of workshop/meeting design and delivery and how to apply them, internally or with clients, from idea workshops to prototyping to business model presentation [...] environment. • Personal competence (self-competence): Students learn to self-reflect their cultural values and learn strategies how to assume ethical responsibility in an international context. Inhalte [...] sustainable development • ... to find solutions to achieve the sustainable objectives. Methodological Competence: • ... to apply the analysis concept for world views and to explain its elements as well

Weitere Literatur und Informationen werden in der Vorlesung oder im Lernmanagementsystem „meet-to-learn“ bekannt gegeben. page Modulhandbuch für den Masterstudiengang Seite 5 Int [...] Weitere Informationen (z.B. Literatur) werden in der Vorlesung oder im Lernmanagementsystem „meet-to-learn“ bekannt gegeben. page Modulhandbuch für den Masterstudiengang Seite 6 Int [...] nen (z.B. Literatur) werden in der Vorlesung, in den Handouts oder im Lernmanagementsystem „meet-to- learn“ bekannt gegeben. page Modulhandbuch für den Masterstudiengang Seite 10 In

students will be able to • understand, categorize and analyze cultures according to chosen cultural dimensions and taxonomies • understand, judge the benefit of different tools used to train personnel [...] solutions to deal with conflict in managing intercultural groups • analyze and adapt own behavior in intercultural situations as well as to evaluate behavior of others and advise them appropriately to be [...] assimilators and critical incidents are investigated in their use to teach cul- tural awareness. Students are given the opportunity to use these tools in both theoretical and practical exercises. Additionally

students will be able to  understand, categorize and analyze cultures according to chosen cultural dimensions and taxonomies  understand, judge the benefit of different tools used to train personnel [...] solutions to deal with conflict in managing intercultural groups  analyze and adapt own behavior in intercultural situations as well as to evaluate behavior of others and advise them appropriately to be [...] assimilators and critical incidents are investigated in their use to teach cul- tural awareness. Students are given the opportunity to use these tools in both theoretical and practical exercises. Additionally

Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2018. A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras and Tensor Flow, O’Reilly, 2018 Raschka: Machine Learning mit Python: das [...] Regularisierung) • Grundlegende Verfahren des Supervised Learning • Grundlegende Verfahren des Unsupervised Learning • Data Preprocessing • Machine Learning in Python Lehrmaterial / Literatur [...] Hinweis auf ein Bonussystem führen page Machine Learning Machine Learning Zuordnung zum Curriculum Classification Modul-ID Module ID Art des Moduls

Teaching, Inverted Classroom, Peer Instruction, Collaborative Learning, Problem Based Learning, Learning on Demand, Micro-Learning) 3. Blended-Learning: Modelle, Vor- und Nachteile, Best-Practice Beispiele [...] (2018). Handbuch E-Learning: Lehren und Lernen mit digitalen Medien. UTB. Arshavskiy, M. (2017). Instructional Design for eLearning: Essential guide for designing successful eLearning courses. CreateSpace [...] Dirksen, J. (2016). Design for How People Learn. New Riders. eLearning Industry Inc, https://elearningindustry.com. Zuletzt geprüft am 11.08.2020. eLearning Journal Online, https://www.elearning-journal

Inverted Classroom, Peer Instruction, Collaborative Learning, Problem Based Learning, Learning on Demand, Micro-Learning) 3. Blended-Learning: Modelle, Vor- und Nachteile, Best-Practice Beispiele [...] (2018). Handbuch E-Learning: Lehren und Lernen mit digitalen Medien. UTB. Arshavskiy, M. (2017). Instructional Design for eLearning: Essential guide for designing successful eLearning courses. CreateSpace [...] Dirksen, J. (2016). Design for How People Learn. New Riders. eLearning Industry Inc, https://elearningindustry.com. Zuletzt geprüft am 11.08.2020. eLearning Journal Online, https://www.elearning-journal

project teams in digital business. They learn to assess speed, adap- tivity, user-centricity and flexibility as key drivers of a changed process management. You will learn to adopt new, dynamic and flexible [...] ung / Internationalität: Course Content Students learn to argue the impact of digitalization on the management of project teams and to apply methods and tools for the analysis, design, imp [...] flexible ways of thinking as a contrast to traditional, planning-oriented project management in order to meet the requi- rements of very high innovation speed and the rapid changes in customer preferences.

teams in digital business. They learn to assess speed, adap- tivity, user-centricity and flexibility as key drivers of a changed process management. You will learn to adopt new, dynamic and flexible [...] ung / Internationalität: Course Content Students learn to argue the impact of digitalization on the management of project teams and to apply methods and tools for the analysis, design, imp [...] flexible ways of thinking as a contrast to traditional, planning-oriented project management in order to meet the requi- rements of very high innovation speed and the rapid changes in customer preferences.

networks and deep learning methods. • Methodological competence: Students will be able to implement selected deep learning methods based on software libraries, apply them to given data sets, and [...] Machine Learning with Scikit-Learn, Keras and Tensor Flow, O'Reilly, 2018. Raschka: Machine Learning with Python: the practical handbook for Data Science, Predictive Analytics and Deep Learning, mitp-Verlag [...] Gewichtung Learning objectives/competencies to be assessed Zu prüfende Lernziele/Kompetenzen annotation https://scikit-learn.org/stable/user_guide.html https://scikit-learn.org/stable/user_guide

field of deep learning on their own, while also learning from the views and approaches of others to further deepen their understanding. Overall, it helps students to learn not only how to self-organize [...] Machine Learning, Springer, 2006. • F. Chollet: Deep Learning with Python, Manning, 2018. (deutsche Version bei mitp Professional, 2018) • Géron: Hands-On Machine Learning with Scikit-Learn, Keras [...] practical machine learning tools and techniques, Morgan Kaufmann, 2018. A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras and Tensor Flow, O'Reilly, 2018. Raschka: Machine Learning with Python:

Lernziele/Kompetenzen Learning outcomes / competences to be assessed Übungsleistung Drafting of 3-5 experimental designs with a mi- nimum of two different sensors each - for a to- tal of 100% grade [...] inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Übungsleistung Die zu erbringenden Übungsleistungen (Exerci- ses) setzen [...] inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Klausur 90 min. Klausur über 90 Minuten (Einzelleistung), Ge- wichtung

Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2018. • A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras and Tensor Flow, O’Reilly, 201. • S. Raschka: Machine Learning mit Python [...] Einsatzgebiete von Reinforcement Learning Problemstellung und Grundbegriffe Markov-Prozesse Temporal Difference Learning (z.B. Q-Learning, SARSA) Deep Reinforcement Learning Lehrmaterial/Literatur Teaching [...] Nachbereitung sowie KI.Meeting) Lernziele/Qualifikationen des Moduls Learning Outcomes Das Modul besteht aus zwei Vorlesungsteilen KI.Ethik und KI.Kognition sowie einem KI.Meeting. Nach dem erfolgreichen