Search

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

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

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

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

Introduction to basic Czech, German and Polish language Light lunch Afternoon session 13:30 15:30 Visit to one of the UWB research centres (choice: either RICE or NTC) Visit to a company Pilsen [...] Afternoon session 13:00 16:30 19:00 Campus Tour Visit to one of the UWB research centres (choice: either NTIS or RTI) Visit to a company Free time Dinner in a restaurant in the city centre [...] Pilsen to Amberg / Weiden and back will be provided by the organizing team. Wednesday April 22nd Day 3 Amberg, Germany Morning session 10:00 12:30 Departure from Pilsen to Amberg

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

Christian PrA Project work in a small team Deep Learning 1. Bergler, Christian 2. Levi, Patrick Kl 26.01.2026 90 min 14:00 15:30 Deep Reinforcement Learning 1. Nierhoff, Thomas 2. Bergler, Christian ModA [...] (including presentation) International Affairs & Intercultural Meeting (BC) 1. Heckmann, Dominikus 2. Wolff, Annabelle ModA Machine Learning (Englisch) 1. Levi, Patrick 2. Bergler, Christian PrA Project [...] Friday, 30.01.2026 Exam Exam Exam Exam Exam 14:00–15:30 Deep Learning Ausgewählte Themen der Künstlichen Intelligenz Advanced Deep Learning Time Monday, 02.02.2026 Tuesday, 03.02.2026 Wednesday, 04.02

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

Machine Learning und Data Mining: Verständnis für die Anwendung von Machine-Learning- und Data-Mining-Techniken auf geografische Daten, einschließlich Supervised Learning, Unsupervised Learning, Deep Learning [...] Choroplethenkarten oder interaktive Dashboards. • Geodatenanalyse mit Machine Learning: Anwendung von Machine-Learning-Algorithmen auf geografische Daten zur Vorhersage von Ereignissen, Mustererkennung [...] W) 60 h Eigenstudium 30 h Prüfungsvorbereitung Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

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:

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 [...] Nachbereitung 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 [...] Nachbereitung des Präsenzunterrichts und Projektarbeit) Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

Scikit-learn Lehrmaterial / Literatur Teaching Material / Reading C. M. Bishop: Pattern Recognition and Machine Learning, Springer Verlag, 2016 A. Géron: Hands-on Machine Learning with Scikit-Learn, Keras [...] 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 Bibliothek

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 [...] Rodenburg, Menden - Maeda, J. (2019). How to Speak Machine: Laws of Design for a Digital Age. Portfolio. - Majumder, A. & Gopi, M. (2018): Introduction to Visual Computing: Core Concepts in Computer

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

/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

Dominikus 2. Nierhoff, Thomas Präs Machine Learning 1 1. Bergler, Christian 2. Brunner, Fabian Keine Kl 02.02.2026 60 min 08:30 09:30 Machine Learning 2 1. Levi, Patrick 2. Bergler, Christian ModA [...] einseitig selbst beschrie- ben, nicht progr. TR Kl 30.01.2026 60 min 14:00 15:00 Ethik, Kognition & Meeting 1. Heckmann, Dominikus 2. Ranisch, Lisa Marie Präs Foreign Language 1 (Deutsch) 1. Fröhlich, Anja [...] handschr. be- schriftet Kl 28.01.2026 90 min 14:00 15:30 International Affairs & Intercultural Meeting 1. Heckmann, Dominikus 2. Wolff, Annabelle ModA KI Projekt Gaming 1. Nierhoff, Thomas 2. Meiller

edu/~wcook/papers/HowToGetaPaperAcceptedToOOPSLA/HowToGetAPaperAcceptedToOOPSLA.htm https://www.cs.utexas.edu/~wcook/papers/HowToGetaPaperAcceptedToOOPSLA/HowToGetAPaperAcceptedToOOPSLA.htm https://dl [...] ____ to ____. This year, ____ received many submissions. In order to speed up the review process, some low-quality papers will be rejected directly based on TPC chairs' judgement. We regret to inform [...] trivial facts Failing to explain necessary concepts clearly for readers outside the niche Not connecting background information to the research problem Include only what is needed to understand your approach

Advanced Deep Learning Advanced Deep Learning German  / English Prof. Christian Bergler see also module manual MKI 4 5 WPM WPM ohne TN‐Begrezung no Deep Reinforcement Learing Deep Reinforcement Learning German [...]  Thomas Nierhoff see also module manual MKI 4 5 WPM WPM ohne TN‐Begrezung Explanatory Legend SuSe / WiSe to be offered in the semester PM Compulsory Module PT Project BC Bridge Module for MAI WPM Electi

Beginn Ende Details Deep Learning (SPO alt) 1. Levi, Patrick 2. Bergler, Christian ModA Analyse und Bearbeitung einer gegebe- nen Aufgabenstellung mit Hilfe von Deep Learning; prototypische Realisierung [...] form Datum Dauer Beginn Ende Details Machine Learning 1 1. Bergler, Christian 2. Brunner, Fabian Keine Kl 02.02.2026 60 min 08:30 09:30 Machine Learning 2 1. Levi, Patrick 2. Bergler, Christian ModA [...] hnik & Cyber-Physische Systeme 1. Wiehl, Michael 2. Nierhoff, Thomas ModA Ethik, Kognition & Meeting 1. Heckmann, Dominikus 2. Ranisch, Lisa Marie Präs Grundlagen der Robotik 1. Wenk, Matthias 2

1890852. Ivanov, Dmitry (2024): Digital Supply Chain Management and Technology to Enhance Resilience by Building and Using End-to-End Visibility During the COVID-19 Pandemic. In: IEEE Trans. Eng. Manage. [...] Roming, Lukas; Franke, Jörg; Reitelshöfer, Sebastian (2025b): The path to a more efficient circular economy by integrating deep learning into robotic sorting system. In: Global Conference on Sustainable [...] Bernt Mayer 24 A Micro Data Approach to the Identification of Credit Crunches von Timo Wollmershäuser und Horst Rottmann 25 Strategies and possible directions to improve Technology Scouting in China

system Examination boards Learning spaces Insurances You are here: Studies Getting started University formalties Info session: Exam registration and study organization In addition to revising for exams, success [...] from Wednesday, 10th December 2025 to Wednesday, 7th January 2026 inclusive . (Please note: MANDATORY exam registration for all). All deadlines and dates related to exams can be found on the website ( [...] lecture-free periods Examinations Examinations Repeat examinations Examination system Examination boards Learning spaces Insurances University formalties Overview Info session: Exam registration and study organization

Environmental Engineering There is no entitlement to all compulsory elective modules and elective modules being offered. Similarly, there is no entitlement to courses being offered if the number of participants [...] Digital Technology 5 4 3.2.2 Digital Signal Processing 5 4 3.2.3 PLC-Programming 5 4 3.2.4 Machine Learning 5 4 3.2.5 Energy Conversion in Power/Working Machines 5 4 3.2.6 Smart Grids 5 4 3.3.1 Rheology

introduction to object-oriented programming, 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 their [...] possible to transfer bonus points to repeat examinations. The exam is intended to test the beforementioned competencies. *1) Please note the applicable overview of examination forms in §§ 20 to 29 ASPO [...] *2) Learning Objectives/Competencies to be Assessed Module work (ModA) Project Work in Groups 50% Presentation, similar to board presentation at annual shareholder meeting 50% written