Search

and visitors to immerse themselves in a multicultural environment, learning about different customs, traditions, and lifestyles. Whether you’re looking to expand your global perspective, meet new friends [...] Want to Participate? Are you interested in showcasing your culture through food, fashion, or performance ? We’d love to have you be part of International Day! This is a fantastic opportunity to: Present [...] Welcome to International Day! Join us at Campus Weiden on May 22 from 13:00 – 18:30 for a vibrant celebration of cultures from around the world! Experience an exciting lineup of activities, meet international

generative, AI-based design. Here you will learn to develop visual concepts for apps, websites and interactive experiences, to design intuitive interfaces and to reach and inspire people using media design [...] development, for example. Application Admission requirements Would you like to apply for the Digital Design course? To be admitted to the course, you need a general higher education entrance qualification, [...] compelling visual communication. Designers play a crucial role in optimizing products and services to meet the needs and expectations of users and communicate them consistently and effectively. Depending

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

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

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

Getting to know and learning to assess basic business management methods. • Social competence: Students learn how to convey information clearly, actively listen, and adapt their communication style to different [...] al Competence: Interns learn to apply theoretical knowledge learned in the classroom to real-world situations, enhancing their expertise. • Social Competence: Interns learn to take initiative, work [...] Gewichtung Learning Objectives/Competencies to be Assessed Zu prüfende Lernziele/Kompetenzen portfolio examination Learning portfolio (100%) The Learning Portfolio is used to check the entire

Bayern SU Kl oder ModA oder Präs oder mdlP 1 16 INT International Affairs & Intercultural Meeting 5 4 Heckmann Heckmann SU/Ü ModA 2 21 SK1 Symbolische Künstliche Intelligenz 1 (Logik & Semantik) [...] 2 24 INF Informatik Grundlagen 5 4 Wiehl Wiehl SU/Ü Kl 90 Minuten 2 25 EKM Ethik, Kognition & Meeting 5 4 Heckmann Heckmann SU/Ü Präs 2 26 BWI Betriebswirtschaftslehre & Innovationsmanagement 5 [...] Software Engineering für KI 5 5 Rebholz Rebholz, Neumann SU/Ü Kl 90 Minuten 5 51 ML1 Machine Learning 1 5 4 Brunner Brunner, Bergler SU/Ü Kl 60 Minuten 5 52 BCN Big Data, Cloud & NoSQL 5 4 Neumann

AI Security and Privacy AML Advanced Topics in Machine Learning AURE Autonomous robots CVAE Computer Vision and AI DEV Deep Vision DPLE Deep Learning EMI Embedded Intelligence FL2 Foreign Language 2 SARA [...] MAI BC Fächer Name Langname BLOCK Blockveranstaltung INT International Affairs & Intercultural Meeting PRS Programming Starter ROS Robotics Starter WEB_Ue Web-Technologies (Übung) WEB_VL Web-Technologies

Mobile and Ubiquitous Computing (Vorlesung) ML1 Machine Learning 1 PMA Projektmanagement und agile Entwicklungsmethoden RLE Reinforcement Learning SEK Software Engineering für KI SWE_2 Software-Engineering [...] 20:30 IK 1 Fächer Name Langname FL1 Foreign Language 1 INT International Affairs & Intercultural Meeting MAT Mathematics Starter & Technical Language PRS Programming Starter ROS Robotics Starter WEB_Ue [...] (Übung) MFI1_VL Mathematik für Ingenieure 1 (Vorlesung) PK 2 Programmieren für KI 2 RLE Reinforcement Learning SK2_Ue Symbolische Künstliche Intelligenz 2 (Übung) SK2_VL Symbolische Künstliche Intelligenz 2

accordimg to catalogue One Intercultural Competence Course accordimg to catalogue One Intercultural Competence Course accordimg to catalogue . One advanced course according to Catalogue [...] advanced course according to Catalogue One advanced course according to Catalogue One of the soft skills according to catalogue One advanced course according to Catalogue One advanced [...] advanced course according to Catalogue One advanced course according to Catalogue One advanced course according to Catalogue One advanced course according to Catalogue Phase I: 3.

1 14 INF Informatik Grundlagen 5 4 Wiehl Wiehl SU/Ü Kl 90 Minuten 1 15 EKM Ethik, Kognition & Meeting 5 4 Heckmann Heckmann SU/Ü Präs 1 16 BWI Betriebswirtschaftslehre & Innovationsmanagement 5 [...] Software Engineering für KI 5 5 Rebholz Rebholz, Neumann SU/Ü Kl 90 Minuten 4 41 ML1 Machine Learning 1 5 4 Brunner Brunner, Bergler SU/Ü Kl 60 Minuten 4 42 BCN Big Data, Cloud & NoSQL 5 4 Neumann [...] SpringSchool 5 4 Heckmann Heckmann, Dozierende der Fakultäten EMI/MBUT Sem Präs 6 61 ML2 Machine Learning 2 5 4 Brunner Brunner, Levi SU/Ü, Pr ModA 6 62 KPG KI Projekt Gaming 5 4 Meiller Meiller, Nierhoff

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, and [...] 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:

Definitive Guide to ARM Cortex-M3 and Cortex-M4 Processors, Newnes, 2013 D. W. Lewis: Fundamentals of Embedded Software with the ARM Cortex-M3, Pearson, 2012 M. Trevor: The Designer’s Guide to the Cortex-M [...] Kontaktstudium: 60 h (4 SWS) Eigenstudium: 90 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] ung des Präsenzunterrichts und Projektarbeit) Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

Definitive Guide to ARM Cortex-M3 and Cortex-M4 Processors, Newnes, 2013 D. W. Lewis: Fundamentals of Embedded Software with the ARM Cortex-M3, Pearson, 2012 M. Trevor: The Designer’s Guide to the Cortex-M [...] Kontaktstudium: 60 h (4 SWS) Eigenstudium: 90 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] ung des Präsenzunterrichts und Projektarbeit) 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 page 8

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

Institute of Applied Language Studies City Center Tour – from UWB campus to Brewery Pilsner Urquell Brewery Tour Dinner Departure to Amberg Wednesday June 4th Day 3 Amberg + Weiden, Germany [...] Workshop “Vitality for Performance” by Prof. Jan von Zwieten Trip to Weiden Campus Tour, Social Event KOMOpivo (Beer tasting) Departure to Amberg page Czech-German Staff Week 2025 [...] AW, UWB + EUPeace Alliance Participants’ presentations of their institutions Coffee Break and Meeting with faculty representatives Campus Tour and City Center Tour Dinner Tuesday June 3rd

Institute of Applied Language Studies City Center Tour – from UWB campus to Brewery Pilsner Urquell Brewery Tour Dinner Departure to Amberg Wednesday June 4th Day 3 Amberg + Weiden, Germany [...] Workshop “Vitality for Performance” by Prof. Jan von Zwieten Trip to Weiden Campus Tour, Social Event KOMOpivo (Beer tasting) Departure to Amberg page Czech-German Staff Week 2025 [...] AW, UWB + EUPeace Alliance Participants’ presentations of their institutions Coffee Break and Meeting with faculty representatives Campus Tour and City Center Tour Dinner Tuesday June 3rd

forschung Studi-Kino Studi-Kino Studi-Kino Kirschblüten- festival Speed Meeting Billiard & Dart Good to Know 8. April Bewerbungsworkshop AM 9. April Bewerbungsworkshop WEN 29. April [...] page Bierpong- Stammtisch Opening Party Studieninfotag Studikino Ersti-Tag Good to Know 20. März Medieninfotag 24.-27. März Vorverkauf Opening 10 17 24 31 11 18 [...] Studi Kino Studi-Kino Studi-Kino Start Prüfungs- anmeldung Careerday Weiden Good to Know 21. Mai Simulierte Vorstellungsgespräche mit Wirtschaftsjunioren 5 12 19 26 6

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

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

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 des Supervised Learning (z.B. baumbasierte Ansätze, SVM, Ensemble-Methoden) • Grundlegende Verfahren des Unsupervised Learning (z.B. PCA, k-means Clustering) • Machine Learning in Python mit der [...] Hands-on Machine Learning with Scikit-Learn, Keras and Tensor Flow, O’Reilly, 2018 W. McKinney: Datenanalyse mit Python, O’Reilly, 2018 S. Raschka: Machine Learning mit Python: das Praxis-Handbuch für

Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2018. A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras and Tensor Flow, O’Reilly, 2019. S. Raschka: Machine Learning mit Python [...] Verfahren des Supervised und des Unsupervised Learning • Implementierung und Anwendung von Machine Learning-Methoden in einer Software-Bibliothek (z.B. Scikit-learn) Lehrmaterial / Literatur Teaching [...] TensorFlow 2 und Scikit-learn: das Praxis-Handbuch für Data Science, Deep Learning und Predictive Analytics, mitp-Verlag, 2021. C. M. Bishop: Pattern Recognition and Machine Learning, Springer Verlag, 2016

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 [...] 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 • [...] 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