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, eLearning- Elemente, Scrum-Projekt 150 h, davon: Präsenzzeit: 60h (4 SWS * 15 Vorlesungswochen) Selbststudium/Projektarbeit: 90 h Lernziele / Qualifikationen des Moduls Learning Outcomes [...] 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 [...] davon Präsenz: 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

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

an_print true Author kl producer Microsoft: Print To PDF access_permission:can_modify true pdf:docinfo:producer Microsoft: Print To PDF pdf:docinfo:created 2019-12-12T13:50:16Z Microsoft [...] 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 [...] org.apache.tika.parser.pdf.PDFParser creator kl meta:author kl pdf:producer Microsoft: Print To PDF meta:creation-date 2019-12-12T13:50:16Z created 2019-12-12T13:50:16Z access_permissio

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

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 [...] 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 [...] 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

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 [...] 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 [...] bonus earned is forfeited. It is not possible to transfer bonus points to repeat examinations. The group project is used to test the practical learning content and competence profiles, including teamwork

t: 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 [...] t: 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 [...] t: 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

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

schreiben und einen Lebenslauf verfassen.  kennen Standardsätze für Diskussionen (z. B. in Meetings), Telefo- nieren und Präsentieren.  können technische Komponenten anhand von Beschreibungen [...] Erstellen eines Lebenslaufs, Telefonieren, Ge- schäftsbriefe (Arten und Aufbau), typische Floskeln in Meetings, Erklä- ren von Grafiken, Präsentationen Technisches Englisch: Eigenschaften von Materialien [...] EI, Bac AI (Pflicht) Studiensemester s. Studienplan Lehrform/SWS Selbststudium / Blended Learning: 0 SWS Arbeitsaufwand (Workload) 150 h Empf. Voraussetzungen keine Angestrebte Lern

depending on prior knowledge and study objective. We will be happy to advise you on your choice of module. It can also be helpful to take an Online-Selbsttest . Mathematics I Most intermediate-level topics [...] important for a successful start to your studies. In this module, you will gain an insight into physical ways of thinking and working using the example of mechanics and learn about essential physical quantities [...] decimals, terms) Length: 48 teaching units of 45 minutes each Course location: Weiden Dates : February to April; every 14 days on Saturdays (see also Further information ) Participation fee: 350,-€ Mathematics

mentoring program? ❖ to have the opportunity to share your experiences with young (future) researchers and to offer them new perspectives and career paths. ❖ to contribute to enhancing the attractiveness [...] ss of our university and expanding international partnerships. ❖ to inspire more people for your field of expertise. ❖ to meet new open-minded people and establish new cross-border connections through [...] mentoring program 'careerSTEPS'?" ❖ Because we value and want to support your talent. ❖ Because you will have the opportunity to learn about what and how experts work in a specific field and what their

mentoring program? ❖ to have the opportunity to share your experiences with young (future) researchers and to offer them new perspectives and career paths. ❖ to contribute to enhancing the attractiveness [...] ss of our university and expanding international partnerships. ❖ to inspire more people for your field of expertise. ❖ to meet new open-minded people and establish new cross-border connections through [...] mentoring program 'careerSTEPS'?" ❖ Because we value and want to support your talent. ❖ Because you will have the opportunity to learn about what and how experts work in a specific field and what their

TECHNOLOGY CLOSER TO THE PUBLIC! Play Video Schließen Medicine without technology? Unthinkable! From hearing aids to pacemakers, from MRI scanners to hybrid operating theatres, from hygiene to laboratory analyses [...] the opportunity to gain further qualification with a radiation protection course . Excursions will take you to a wide variety of areas in medical technology, from industrial production to hospital applications [...] intercultural activities and mentoring. We aim to make the challenging transition from your country to our country and the jump from secondary school to university easier. Sound interesting?! Join our

passionate about the technology that helps people to live a better life? Are you looking for a diverse role in an international environmen t? Do you want to learn something new every day and enjoy interdisciplinary [...] interdisciplinary collaboration? Have you always wanted to study abroad ? If you have answered yes to any of these questions, then you've come to the right place! Study with us and find amazing global healthcare [...] will programme computers, learn how to work with robots and use various 3D printers. You will also have contact with industry and medical facilities through excursions, listen to guest lectures from experts

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 [...] mailto:f.seitz@oth-aw.de page English Abstract: We analyze the ability of standard macro models to explain recessions and depressions as well as financial crises. We find that the usual textbook models [...] decisive factor is the (lack of) adjustment of the real interest rate. As a solution, we propose recourse to elements of the Loanable Funds theory. This can better explain the interactions between the goods

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 [...] mailto:f.seitz@oth-aw.de page English Abstract: We analyze the ability of standard macro models to explain recessions and depressions as well as financial crises. We find that the usual textbook models [...] decisive factor is the (lack of) adjustment of the real interest rate. As a solution, we propose recourse to elements of the Loanable Funds theory. This can better explain the interactions between the goods

attributed to the normal response of currency holdings to the lowering of interest rates and to the increase in income from the government stimulus. The remaining 80% may be due to an increase [...] circulation of small, medium and large DM notes amounted to around €3 billion to €9 billion at the end of January 2002. It declined to around €1 billion to €2 billion at the end of January 2003. This is [...] period does not allow to establish a stable cointegration relation, which seems to be due, in particular, to the early part of the sample. 20 We presume that in order to estimate a suitable vector

Prüfungsvorbereitung: 30 h Gesamtaufwand: 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] 60 h Prüfungsvorbereitung: 30 h Gesamtzeit: 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden [...] Prüfungsvorbereitung: 30 h Gesamtaufwand: 150 h Lernziele / Qualifikationen des Moduls Learning Outcomes Nach dem erfolgreichen Absolvieren des Moduls verfügen die Studierenden über die folgenden

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

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

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

Elektromobilität Geo-Verfahren: Routing, Connectivity Maps Künstliche Intelligenz: Machine Learning, Data Mining Kommunikation: C-V2X (LTE, LTE-A) Fahrzeug: Sensorik, Bussysteme, ROS [...] Programmiersprache und Konzipierung verteilter Systeme  Implementierung unter Verwendung von Device-to-Device Kommunikation  Einarbeiten in Infrastrukturkommunikation Automotive Engineering @ OTH 14 [...] n in Fahrzeugkommunikation Automotive Engineering @ OTH 14.11.2019 MAPR Vorstellung 7 How to MAPR @Automotive? 1. Melde dich bei uns! 2. Auswahl des MAPR-Forschungsthemas mit Prof. Dr. Höß

(2019). Hands-on machine learning with scikit-learn, keras, and TensorFlow (2nd ed.). Sebastopol, CA: O’Reilly Media.  Bishop., C. (2016). Pattern Recognition and Machine Learning. New York, NY: Springer [...] Studierenden solide Grundlagen in Deep Learning erworben. Insbesondere sind sie in der Lage:  den Stand der Technik von Machine Learning und Deep Learning zu verstehen  können die verschiedenen [...] concepts and their functionality  Introduction to Robot operation system (ROS)  Introduction to mapping and pathfinding algorithms  Introduction to robotic simulation tools  Insight into robot

Studierenden solide Grundlagen in Deep Learning erworben. Insbesondere sind sie in der Lage:  den Stand der Technik von Machine Learning und Deep Learning zu verstehen  können die verschiedenen [...] Dipl.-Ing. David Wagner Johannes Dettelbacher, M.Sc. Bezeichnung engl.: Introduction to Machine Learning in Python Referent(en):  Dipl.-Ing. David Wagner  Johannes Dettelbacher, M.Sc.  Hochschule [...] Dr. Bogner DLBC-I Deep Learning Bootcamp Modulverantwortung: Prof. Dr. Alexander Schiendorfer Bezeichnung engl.: Deep Learning Bootcamp Referent(en): Prof. Dr. Alexander

Studierenden solide Grundlagen in Deep Learning erworben. Insbesondere sind sie in der Lage:  den Stand der Technik von Machine Learning und Deep Learning zu verstehen  können die verschiedenen [...] int true Author wbogner producer Microsoft: Print To PDF access_permission:can_modify true pdf:docinfo:producer Microsoft: Print To PDF pdf:docinfo:created 2023-01-21T10:03:02Z Microsoft [...] Maschinelles Lernen Modulverantwortung: Sebastian Wilhelm Bezeichnung engl.: Introduction to Machine Learning Referent(en): Wilhelm, Sebastian: Kontakt: sebastian.wilhelm@th-deg.de Voraussetzungen: