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They learn to identify these situations and to appear interculturally competent. • Personal competence (social competence and self-competence): Students acquire the interdisciplinary ability to perform [...] • In addition to theoretical knowledge, students also acquire the ability to apply process engineering principles in practice, both independently and in a team. • They are able to critically analyse [...] interculturality. They learn the theoretical basics of intercultural communication and understanding. • Methodological competence: Students are able to apply strategies to improve the handling of

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

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 [...] Vorgehens und verschiedener Algorithmen des Machine Learning. • Methodenkompetenz: Die Studierenden sind befähigt, verschiedene Verfahren des Machine Learnings praktisch anzugehen und die Ergebnisse zu bewerten

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 [...] und verschiedener Algorithmen des Machine Learning. • Methodenkompetenz: Die Studierenden sind befähigt, verschiedene Verfahren des Machine Learnings praktisch anzugehen und die Ergebnisse zu

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 [...] 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: [...] 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, and

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 60 [...] 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 [...] Usability 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

J., Osborn, D., Murphy, P. & Edwards, A. (2017). The Cool Stuff in Premiere Pro: Learn advanced editing techniques to dramatically speed up your workflow (2nd ed.). Apress. doi: 10.1007/978-1-4842-2890-6 [...] /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

/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 [...] h 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

achievements of previous studies can only be credited 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. [...] Berechnung Workload: According to § 8 para. 1 sentence 3 BayStudAkkV, the following applies: One credit point is based on a workload of 25 to 30 hours. To calculate the workload, a distinction [...] 3.4 Master´s-Degree and Service Learning Masterabschluss und Service Learning ID Name 02003 Colloquium 00850 Master´s Thesis 00851 Service Learning 8 4 Virtual University

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

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 Teaching [...] ggf. auch einen Hinweis auf ein Bonussystem führen Machine Learning Machine Learning Zuordnung zum Curriculum Classification Modul-ID Module ID Art des Moduls Kind

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 4 [...] (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

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 4 [...] (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

Berechnung des Workload According to § 8 para. 1 sentence 3 BayStudAkkV, the following applies: One credit point is based on a workload of 25 to 30 hours. To calculate the workload, a distinction [...] the preparation and follow-up of the learning material • Exam preparation = hours spent preparing for an examination event • Examination workload = hours required to complete the examination • Internships [...] university must be submitted to the Students` Office after enrollment at OTH. Subsequently, the respective lecturers will check whether the subjects already taken correspond to the requirements of our subjects

Self-Competence): Students are able to continuously expand their expertise in the field of deep learning by engaging with current scientific literature and to remain up to date in this dynamic area of research [...] 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, [...] 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:

neural 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 [...] ability to abstract. Students learn a confident approach to modern database applications and infrastructures. • Personal competence (social competence and self-competence): Students will be able to model

II, III (Representation Learning, Transfer Learning, Distillation Learning, Contrastive Learning, Self-Supervised Learning, Active Learning, Causal Learning, N-Shot Learning, Weak Supervision) • Kapitel [...] 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

ce): The students are able to combine knowledge and skills from the basic modules to derive and develop new solutions. The have the competence to discuss issues related to energy storage in interdisciplinary [...] Psychological Association. The Official Guide to APA Style (7th Ed.) Washington. Carlson, K. A. & Winquist, J. R. (2017). An Introduction to Statistics. An Active Learning Approach. SAGE. Creswell, J. W. & Plano [...] Ability to recognise legal problems in energy/environmental law, identification of the most important applicable regulations Independent application of regulations relevant to practice Ability to identify

Selbststudium Prüfungsvorbereitung = 90 h = 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] Selbststudium Prüfungsvorbereitung = 90 h = 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] Nachbereitung Prüfungsvorbereitung = 60 h = 90 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden