int true Author wbogner producer Microsoft: Print To PDF access_permission:can_modify true pdf:docinfo:producer Microsoft: Print To PDF pdf:docinfo:created 2022-01-22T09:27:39Z Microsoft [...] 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 [...] Fluid Dynamics Modulverantwortung: Prof. Dr.-Ing. Stefan Beer Bezeichnung engl.: Introduction to Computational Fluid Dynamics (CFD) Referent(en): Prof. Dr.-Ing. Stefan Beer, OTH Amberg-Weiden V

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:

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

(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

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

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

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

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

you to act professionally in an industrial or trade-related context and to become active in design. As a Master's student, you will also be characterised by an improved ability to work in a team, to think [...] Logistics & Digitalization is not merely to impart specialist knowledge, but to develop your personal skills in a holistic way. Thus, the course offers you the opportunity to refine your social skills for cooperation [...] & Digitalizationn are characterized by their ability to develop and evaluate scientific approaches and methods and to successfully transfer them to companies or the scientific community (committees, a

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

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

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

inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Klausur 90 min. Gewichtung: 100% Über die Klausur werden die gesamten [...] inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Klausur 90 min. Gewichtung: 100% Über die Klausur werden die gesamten [...] inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Finanz-/Investitions- wirtschaft: Gewichtung: 100% Hinweis: Die

will be on relating theoretical concepts to assessment and evaluation of practices in organizations. Empirical projects will be analyzed to extract lessons learned and suggestions for improvement. Explorations [...] project work is used to test the entire learning content and competency profiles, including the com- petencies for presentation. The assessed discussion contributions serve to dee- pen the understanding [...] you are not eligible to sign up for more than 11 Advanced Modules or more than 4 Soft Skill Modules prior to having completed a minimum of 120 of 150 possible ECTS. According to §6 (2) of the old Study

inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Klausur Klausurteil: Betriebswirtschaft Dauer: 30 Minuten Gewichtung: [...] inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Klausur 90 min. Gewichtung 100% Über die Klausur werden die gesamten [...] inkl. Gewichtung *2 Type/scope incl. weighting Zu prüfende Lernziele/Kompetenzen Learning outcomes / competences to be assessed Kolloquium Gewichtung: 100% Über die Klausur werden die gesamten L

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

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

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

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

, 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

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