Module Elective courses 1, Media Computer Science (Bachelor) (ER 6)

The lecture teaches the construction of mobile media applications. The main concepts are discussed using the Android platform. In a first partt, the basic technologies and limitations of mobile devices are shown. The second part examins different development strategies like native applications, device independend abstractions and web applications. A main part of the lecture is the integration of different media types into mobile applications and the constraints the developer has to keep in mind.

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Leacture with exercise

The buzzword "Cloud" represents a variety of interesting technologies which gained importance in the life of a computer science professional. Those are being collected, examined, explained and understood during the course. Primary objective is usefulness for the student, regardless of whether he acts as a cloud user, developer, administrator or even entrepreneur. Understand the broad meaning of "Cloud Computing" from a variety of perspectives: Definition, use cases, technology basics, key players, APIs, scaling, redundancy …

Powerpoint slides

Students will be able to understand the fundamental concepts of software development for embedded real-time systems. In the context of this lecture, embedded systems are all computers controlled by software that are part of a larger system and whose primary function is not computing. Real-time systems also involve aspects of temporal behavior, i.e., they are systems that not only have to provide a correct answer, but also have to calculate the system answer within a predetermined and guaranteed period of time. In detail, topics from the following areas are covered: design and architecture of automotive control units, fundamentals of real-time programming, coding for data transmission, embedded C. The participants of the lecture apply their knowledge on the basis of exercises.

Slides, blackboard, exercise sheets

Lecture

Contents:
ERP basics, system integration, system architectures, and logistics: Distribution (SD), Materials Management (MM), Production Planning and Control (PP) as well as Financial Accounting (FI) and Controlling (CO). In addition, an overview is given to the software selection.

Recommended reading: Lecture material completely as PowerPoint documents, blackboard notes for interactive development of central problem positions, a main textbook to ERP, a main textbook to SAP ECC 6.0.

Kind of work: Lecture participation

The lecture gives students a general introduction to the topic of machine learning as a branch of artificial intelligence. Starting with classic methods, an overview of the most important topics in this area is given, which ends with a brief insight into the current developments surrounding “deep learning”.

In addition to the theoretical basics, the practical use of the methods learned is demonstrated with the help of the Python programming language and the use of Jupyter Notebooks.

The contents of the lecture include:

• Introduction and overview of the subject area
• Introduction to the Python programming language and Jupyter Notebooks
• Linear regression
• Logistic regression
• ML workflow
• Overfitting and underfitting
• Decision trees
• Ensemble learning (random forest, gradient boosting)
• Unsupervised learning
• Neural networks and deep learning

• A. Géron, "Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow", O'Reilly Media, 2nd Edition, 2019.
• J. Frochte, "Maschinelles Lernen - Grundlagen und Algorithmen in Python", Carl Hanser Verlag, 2. Auflage, 2019.

Seminar