Sebastian Ukleja, BSc Junior Researchersebastian.ukleja@fh-campuswien.ac.at T: +43 1 606 68 77-2143 F: +43 1 606 68 77-2139 Room: B.3.08 Favoritenstraße 226 1100 WienLectures 2021/22Engineering> Deep Learning – advanced AI and Data Science ILV Computer Science and Digital Communications moreDeep Learning – advanced AI and Data Science ILVLector: DI Dr. techn. Mugdim Bublin, Dr. Christian Steineder, Sebastian Ukleja, BSc3SWS6ECTSLecture contents- Deep Learning Foundation- Motivation and basic ideas - Basic principles behind algorithms - Deep Learning Algorithms and Networks- Convolutional Neuronal Networks (CNN) - Recurrent Neural Networks (RNN) - Generative Neural Networks (GAN, Autoencoders) - Reinforcement Learning (Deep QNet) - Applications of Deep Learning and Artificial Intelligence for- Medicine, IoT, Industry 4.0, Autonomous Driving, Games etc.Assessment methodsContinuous assessment - Project work - Exercises during lectures - Final written examTeaching methods- Lecture - Group work (project) - Practical exercises - Continuous Discussion and feedbackLanguageEnglish> Deep Learning – advanced AI and Data Science ILV Computer Science and Digital Communications moreDeep Learning – advanced AI and Data Science ILVLector: DI Dr. techn. Mugdim Bublin, Dr. Christian Steineder, Sebastian Ukleja, BSc3SWS6ECTSLecture contents- Deep Learning Foundation- Motivation and basic ideas - Basic principles behind algorithms - Deep Learning Algorithms and Networks- Convolutional Neuronal Networks (CNN) - Recurrent Neural Networks (RNN) - Generative Neural Networks (GAN, Autoencoders) - Reinforcement Learning (Deep QNet) - Applications of Deep Learning and Artificial Intelligence for- Medicine, IoT, Industry 4.0, Autonomous Driving, Games etc.Assessment methodsContinuous assessment - Project work - Exercises during lectures - Final written examTeaching methods- Lecture - Group work (project) - Practical exercises - Continuous Discussion and feedbackLanguageEnglish> Software Engineering ILV Computer Science and Digital Communications moreSoftware Engineering ILVLector: Mag. Dipl.-Ing. Peter Gerstbach, FH-Prof.in Mag.a Dr.in Sigrid Schefer-Wenzl, MSc BSc, Sebastian Ukleja, BSc4SWS7ECTSLecture contentsThis lecture aims to explain the technical, organizational and economic aspects of software engineering. Organizational possibilities for structuring software development in the form of process models, such as waterfall model, spiral model and agile models are presented. The technical aspects of software engineering focus on the creation of object-oriented systems and their modeling. The course covers in particular the following contents: - Software Engineering Activities, - Requirements Engineering, - use cases, - high-level design - UML activity diagrams, - UML class diagrams, - UML sequence diagrams, - Software testing, - software process models and - Agile software development.Assessment methodsFinal exam Individual and group worksTeaching methodsBlended learning, guest lectures, experiental learning, coachingLanguageGerman> Bachelor Thesis 1 SE Computer Science and Digital Communications moreBachelor Thesis 1 SELector: DI Dr. techn. Mugdim Bublin, FH-Prof. DI Thomas Fischer, Leon Freudenthaler, BSc MSc, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka, FH-Prof. Dipl.-Ing. Heimo Hirner, FH-Prof.in Mag.a Dr.in Sigrid Schefer-Wenzl, MSc BSc, Silvia Schmidt, BSc MSc, Dr. Christian Steineder, Bernhard Taufner, BSc, MSc, Sebastian Ukleja, BSc1SWS4ECTSLecture contents- Independent work on a topic from the area of Computer Science, primarily based on the technical topics of the elective modules in the 4th and 5th semesters at a scientific level under the guidance of a supervisor. - Elaboration of the bachelor thesis 1Assessment methodsFinal exam Approbation of bachelor thesisTeaching methodsImplementation of a project and elaboration as a bachelor thesis with coaching. Students present the current development of their bachelor thesis 1 at regular intervals and put it up for discussion.LanguageGerman> Elective Project 2 UE Computer Science and Digital Communications moreElective Project 2 UELector: DI Dr. techn. Mugdim Bublin, Leon Freudenthaler, BSc MSc, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka, FH-Prof. Dipl.-Ing. Heimo Hirner, FH-Prof.in Mag.a Dr.in Sigrid Schefer-Wenzl, MSc BSc, Silvia Schmidt, BSc MSc, Dr. Christian Steineder, Bernhard Taufner, BSc, MSc, Sebastian Ukleja, BSc2SWS5ECTSLecture contentsStudents apply the skills acquired to complete a project in a coordinated and structured manner. project in a coordinated and structured manner. In doing so, they independently define a concrete sub-goal in the project. A well-founded theoretical approach is thus combined with practical application. Collaboration on an industrial R&D project or on current problems within the framework of the R&D activities of the UAS is possible.Assessment methodsFinal exam Practical project in small groups.Teaching methodsGroup work, practical project implementation accompanied with exercises and coaching.LanguageGerman
> Deep Learning – advanced AI and Data Science ILV Computer Science and Digital Communications moreDeep Learning – advanced AI and Data Science ILVLector: DI Dr. techn. Mugdim Bublin, Dr. Christian Steineder, Sebastian Ukleja, BSc3SWS6ECTSLecture contents- Deep Learning Foundation- Motivation and basic ideas - Basic principles behind algorithms - Deep Learning Algorithms and Networks- Convolutional Neuronal Networks (CNN) - Recurrent Neural Networks (RNN) - Generative Neural Networks (GAN, Autoencoders) - Reinforcement Learning (Deep QNet) - Applications of Deep Learning and Artificial Intelligence for- Medicine, IoT, Industry 4.0, Autonomous Driving, Games etc.Assessment methodsContinuous assessment - Project work - Exercises during lectures - Final written examTeaching methods- Lecture - Group work (project) - Practical exercises - Continuous Discussion and feedbackLanguageEnglish
> Deep Learning – advanced AI and Data Science ILV Computer Science and Digital Communications moreDeep Learning – advanced AI and Data Science ILVLector: DI Dr. techn. Mugdim Bublin, Dr. Christian Steineder, Sebastian Ukleja, BSc3SWS6ECTSLecture contents- Deep Learning Foundation- Motivation and basic ideas - Basic principles behind algorithms - Deep Learning Algorithms and Networks- Convolutional Neuronal Networks (CNN) - Recurrent Neural Networks (RNN) - Generative Neural Networks (GAN, Autoencoders) - Reinforcement Learning (Deep QNet) - Applications of Deep Learning and Artificial Intelligence for- Medicine, IoT, Industry 4.0, Autonomous Driving, Games etc.Assessment methodsContinuous assessment - Project work - Exercises during lectures - Final written examTeaching methods- Lecture - Group work (project) - Practical exercises - Continuous Discussion and feedbackLanguageEnglish
> Software Engineering ILV Computer Science and Digital Communications moreSoftware Engineering ILVLector: Mag. Dipl.-Ing. Peter Gerstbach, FH-Prof.in Mag.a Dr.in Sigrid Schefer-Wenzl, MSc BSc, Sebastian Ukleja, BSc4SWS7ECTSLecture contentsThis lecture aims to explain the technical, organizational and economic aspects of software engineering. Organizational possibilities for structuring software development in the form of process models, such as waterfall model, spiral model and agile models are presented. The technical aspects of software engineering focus on the creation of object-oriented systems and their modeling. The course covers in particular the following contents: - Software Engineering Activities, - Requirements Engineering, - use cases, - high-level design - UML activity diagrams, - UML class diagrams, - UML sequence diagrams, - Software testing, - software process models and - Agile software development.Assessment methodsFinal exam Individual and group worksTeaching methodsBlended learning, guest lectures, experiental learning, coachingLanguageGerman
> Bachelor Thesis 1 SE Computer Science and Digital Communications moreBachelor Thesis 1 SELector: DI Dr. techn. Mugdim Bublin, FH-Prof. DI Thomas Fischer, Leon Freudenthaler, BSc MSc, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka, FH-Prof. Dipl.-Ing. Heimo Hirner, FH-Prof.in Mag.a Dr.in Sigrid Schefer-Wenzl, MSc BSc, Silvia Schmidt, BSc MSc, Dr. Christian Steineder, Bernhard Taufner, BSc, MSc, Sebastian Ukleja, BSc1SWS4ECTSLecture contents- Independent work on a topic from the area of Computer Science, primarily based on the technical topics of the elective modules in the 4th and 5th semesters at a scientific level under the guidance of a supervisor. - Elaboration of the bachelor thesis 1Assessment methodsFinal exam Approbation of bachelor thesisTeaching methodsImplementation of a project and elaboration as a bachelor thesis with coaching. Students present the current development of their bachelor thesis 1 at regular intervals and put it up for discussion.LanguageGerman
> Elective Project 2 UE Computer Science and Digital Communications moreElective Project 2 UELector: DI Dr. techn. Mugdim Bublin, Leon Freudenthaler, BSc MSc, FH-Prof. FH-Hon.Prof. Priv.-Doz. Mag. DI. DI. Dr.techn. Karl Michael Göschka, FH-Prof. Dipl.-Ing. Heimo Hirner, FH-Prof.in Mag.a Dr.in Sigrid Schefer-Wenzl, MSc BSc, Silvia Schmidt, BSc MSc, Dr. Christian Steineder, Bernhard Taufner, BSc, MSc, Sebastian Ukleja, BSc2SWS5ECTSLecture contentsStudents apply the skills acquired to complete a project in a coordinated and structured manner. project in a coordinated and structured manner. In doing so, they independently define a concrete sub-goal in the project. A well-founded theoretical approach is thus combined with practical application. Collaboration on an industrial R&D project or on current problems within the framework of the R&D activities of the UAS is possible.Assessment methodsFinal exam Practical project in small groups.Teaching methodsGroup work, practical project implementation accompanied with exercises and coaching.LanguageGerman