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multilingual digital exercises and examination tasks. The aim is to establish a permanent infrastructure that gives all German universities access to digital tasks using the best available technology – with a [...] architecture that professionalises digital teaching and promotes individualised learning. In the long term, the DZdA aims to become institutionalised and independent of third-party funding , and sees itself [...] der Hochschullehre Funding amount: 11.9 million euros Project duration: 1 October 2025 to 1 October 2031 (subject to successful interim evaluation) Project management: Prof. Dr. paed. Dipl.-Math. Mike Altieri

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

Competence and Self-competence):  Students are also able to present solutions that have been created, to discuss their quality and alternatives and to reflect on their problem-solving strategy in a technical [...] This course provides a comprehensive introduction to fundamental algorithms and data structures used in computer science. Students will learn how to implement linear, binary, ternary, and Fibonacci search [...] university is to be examined individually. Lecture, seminar with exercises, computer exercise Contact time: 60 h Self-study: 90 h Total workload: 150 h Learning Outcomes Learning Outcomes

Introduction to basic Czech, German and Polish language Light lunch Afternoon session 13:30 14:30 15:30 Visit to one of the UWB research centres (choice: either NTIS or RTI) Visit to Konplan [...] 13:00 13:45 15:00 16:00 19:00 Campus Tour Visit to one of the UWB research centres (choice: either RICE or NTC) Visit to ZF Engineering company Free time Dinner in a restaurant in [...] zcu.cz/en/ https://konplan.cz/ *The common bus transportation from Pilsen to Amberg / Weiden and back will be provided by the organizing team. Wednesday April 22nd Day

Filmmaker’s Eye: Learning (and Breaking) the Rules of Cinematic Composition. Focal Press, New York · Riley, C. (2009): The Hollywood Standard. The Complete and Authoritative Guide to Script Format and [...] E.R. (2017): Computer Vision: Principles, Algorithms, Applications, Learning. Academic Press · Eck, D.J. (2018): Introduction to Computer Graphics. Online-Ressource · Eckardt, M. (2016): Cinema 4D 18 [...] source · Goodfellow, I., Bengio, Y., & Courville, A. (2016): Deep Learning. MIT Press · Kaehler, A. & Bradski, G. (2016): Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library. O'Reilly Media

ns/5091/what-to-do-to-switch-to-biblatex https://tex.stackexchange.com/questions/5091/what-to-do-to-switch-to-biblatex https://tex.stackexchange.com/questions/5091/what-to-do-to-switch-to-biblatex https://tex [...] ns/5091/what-to-do-to-switch-to-biblatex https://tex.stackexchange.com/questions/5091/what-to-do-to-switch-to-biblatex https://tex.stackexchange.com/questions/5091/what-to-do-to-switch-to-biblatex https://tex [...] ns/5091/what-to-do-to-switch-to-biblatex https://tex.stackexchange.com/questions/5091/what-to-do-to-switch-to-biblatex https://tex.stackexchange.com/questions/5091/what-to-do-to-switch-to-biblatex https://tex

Steuergesetze, OECD-Musterabkommen, Taschenrechner 00820 IB 20.01.2026 12:00 Uhr EDV 106, MF Introduction to Management Fischer, Denise 60 Minuten pocket calculator 00775 MLD s. vhb s. vhb IT-Sicherheit Abe [...] MF 2x , 106 1x Labor Law Stauf, Christian 90 Minuten Legal texts, commentary possible by reference to other paragraphs 00776 MLD s. vhb s. vhb Management von Logistik- und SCM-Projekten Abels-Schlosser [...] beachten: Stand 16.01.2026 - Änderungen möglich / Please note: status as of 16 January 2026 – subject to change. Wiederholungsprüfungen sind fett markiert / Repeat examinations are highlighted in bold Bei

A. Kimothi, A Simple Guide to Retrieval Augmented Generation, First edition. Shelter Island, NY: Manning Publications, 2025, isbn: 978-1-63343-585-8. Adresse: https://learning.oreilly.com/library/view/ [...] Models für SPS Codegenerierung: | OTH Amberg-Weiden 27.01.2026 32 Anhang RAG Quelle: A Simple Guide to Retrieval Augmented Generation [1] Large Language Models für SPS Codegenerierung: | OTH Amberg-Weiden [...] 14209. Large Language Models für SPS Codegenerierung: | OTH Amberg-Weiden 27.01.2026 37 https://learning.oreilly.com/library/view/-/9781633435858/ https://lilianweng.github.io/posts/2023-06-23-agent/

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

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

Raschka, S., Liu, Y. H., & Mirjalili, V. (2022). Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. Packt Publishing Ltd. • Goodfellow, I [...] lineare Regression mit scikit-learn • Einführung in Deep Learning - Grundprinzipien neuronaler Netze (Feedforward, Training, Overfitting) - Erstellung einfacher Deep-Learning-Modelle mit Keras oder PyTorch [...] Material / Reading • Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. " O'Reilly Media, Inc.". • Raschka

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

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

Nachbereitung 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 = 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

Géron, A.: „Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow“, O’Reilly; 2. Edition (2020) • Bishop, C.M.: „Pattern Recognition and Machine Learning“, Springer (2006) Aktuelle L [...] conditions - Methods for solving the flow equations - Introduction to turbulence modeling - Methods for meshing - Applications to sample problems with commercial or open software The contents [...] it is the case, e.g., for container vessels, to very small devices like cooling circuits of microprocessors. Thus, the content is applicable everywhere to many fields. Modulprüfung (ggf. Hinweis zu

Géron, A.: „Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow“, O’Reilly; 2. Edition (2020) • Bishop, C.M.: „Pattern Recognition and Machine Learning“, Springer (2006) Aktuelle L [...] Zusammenhang mit Energiespeicherung (Wasserstoff, Elektrolyse, Brennstoffzellen, power to gas, power to liquid, biomass to liquid, etc.), Flexibilisierung von Kraft-Wärme-Kälte-Kopplungsprozessen durch die [...] conditions - Methods for solving the flow equations - Introduction to turbulence modeling - Methods for meshing - Applications to sample problems with commercial or open software The contents

Zusammenhang mit Energiespeicherung (Wasserstoff, Elektrolyse, Brennstoffzellen, power to gas, power to liquid, biomass to liquid, etc.), Flexibilisierung von Kraft-Wärme-Kälte-Kopplungsprozessen durch die [...] Nachbereitung 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 = 90 h = 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

Mobile and Ubiquitous Computing (Vorlesung) ML1 Machine Learning 1 PMA Projektmanagement und agile Entwicklungsmethoden RLE Reinforcement Learning Seite 58 / 80 OTH Amberg-Weiden, Rechenzentrum D-92224 [...] FL1 Foreign Language 1 GTS German for Technical Studies 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

Name Langname ADL Advanced Deep Learning AICO AI Conference AISP AI Security and Privacy AURE Autonomous robots CVAE Computer Vision and AI DEV Deep Vision DPLE Deep Learning EMI Embedded Intelligence FL2 [...] MAI BC Fächer Name Langname BLOCK Blockveranstaltung INT International Affairs & Intercultural Meeting MESE Medical Systems Engineering PRS Programming Starter ROS Robotics Starter WEB_Ue Web-Technologies

Intelligence L Labor laboratory/ lab Laborordnung laboratory regulations Lernportfolio Learning Portfolio M Medienproduktion und Medientechnik Media Production and Media Technology M [...] Prüfung Practical Exam Präsident president Präsentation presentation Präsenzveranstaltung face-to-face class Praktika practical courses Praxisphase internship phase Professor professor [...] Prüfungsordnung examination regulations Prüfungsunfähigkeitsbescheinigung certificate of inability to take exams R Regelstudienzeit standard period of study Rückmeldung reregistration S

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 [...] (Vor-/Nach- bereitung Präsenzstudium, Prüfungs- vorbereitung) Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] Selbststudium: 120 h Prüfungsvorbereitung: 60 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

Publikationen Publications Bayer, S., Hilgarth, B.: Adaption of Cybernetic e-Learning Management Model to Blended Learning scenarios in international acting industry - a Case Study. (in internal review [...] 151-167, Springer, Wien (2012) Hilgarth, B.: E-Learning Success in Action – from case study research to the creation of the Cybernetic e-Learning Management Model. International Journal of Computer [...] Hilgarth, B.: BPM@KMU - Designing e-Learning for the Intro- duction of BPM in Small- and Medium-sized Enter- prises. In S-BPM ONE - Learning by Doing - Doing by Learning, Proceedings of the Third International

have to take in your current semester. • In order not to miss important information and deadlines, please subscribe to the newsletter on the noticeboard. • Please note that in addition to the weekly [...] Online Rahman How to Study Successfully 140 Löbus How to Create a Startup HSG 001 Löbus Event and Project Management HSG 002 Güner Event and Project Management HSG 002 Güner Meetings, Negotiations [...] Intercultural Competence - Germany & Austria & Switzerland DACH-Group 3 139 Michalska Introduction to Management HSG 001 Fischer Basic Marketing HSG MF Grimm Basic Marketing HSG MF Grimm Production

Python mit Pandas, NumPy, Scikit-learn) • Implementierung eines angewandten Projekts • Evaluierung von Modellen und Ergebnissen Literaturhinweise: • Machine Learning: A Probabilistic Perspective, [...] Kontaktzeit: 60 h Eigenstudium: 90 h Gesamtaufwand: 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] Kontaktzeit: 60 h Eigenstudium: 90 h Gesamtaufwand: 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

companies . From business model design to validation and financing to growth and leadership, you will go through the entire entrepreneurial process and develop the skills to implement innovation responsibly [...] anyone who wants to shape innovation rather than just manage it. This is not about traditional business administration—it's about entrepreneurial thinking, bold decisions, and the ability to develop scalable [...] scalable business models from ideas that can compete in the market. You will learn to recognize opportunities early on, critically examine markets, and systematically translate innovative solutions into viable