Computer Science

Algorithms and data structures

Programming knowledge

B2 Level in English

Throughout the course, students:

• Become familiar with basic algorithms in the field of big data and be able to apply them
• Become familiar with systems that are used to process very large volumes of – in particular – unstructured data and be able to assess when it is appropriate to use them

Experience in Software Enigneering and Programming

B2 Level in English

Throughout the course, students:

• Identify real-world problems and recognise the problems of creating complex solutions using a wide variety of IoT platforms. They are in a position to analyse the environment of the problem and are able to discuss these in advance in cooperation with companies.
• Acquire knowledge of design thinking, agile project management and the independent implementation of projects is acquired in teamwork. They are able to apply interdisciplinary knowledge, integrate the problem solver into the project in an agile manner and to present the results of their work.

The cooperating companies offer the students real problems from the most important IoT domains, such as Smart Agriculture, Smart Building, Smart Energy, Smart Production, eHealth, etc.

The problem is described in detail using defined application cases. In addition, the aspects of IoT Cloud and IoT Security are also examined.
The students are supervised by the lecturer and the coach of the innovation lab.

Throughout the course, students:

• become familiar with importance of resource managmeent and concept of elasticity in the cloud
• learn about strategies for synchronizing distributed data sources
• gain availability in explaining advantages and disadvantages of virtualized infrastructures
• launch applications that uses cloud infrastructure for processing or data storage in the cloud
• learn important computing paradigms for higly distributed processing

Teaching Content

• Computing and Internet Scale - clusters, grids, and networks
• Cloud services (such as AWS, Azure, or Google Cloud)
• IaaS, SaaS, PaaS and resource elasticity
• Virtualization, replication and process migration
• Security in the Cloud, Virtual Private Network
• Weakly consistent data stores, CAP Theorem
• Distributed File Systems, e.g. HDFS
• Mapreduce and Hadoop: paradigm for distributed computation

Experience in Programming and Software Engineeering

B2 Level in English

After successful completion of this course, students:

• Know the problems of creating complex systems
• Can apply the basics of scientific work and know how to independently carry out projects appropriate to the degree programme
• Have learned to work in a team and have acquired knowledge in estimating the scope of projects as well as in the management and supervision of projects.
• Are able to apply interdisciplinary knowledge and present work results.

After successful completion of this course, students are able to:

• Identify real-world problems and recognize the core issue of creating complex solutions using a wide range of IoT platforms.
• Analyze the context of a given problem and discuss these in advance in cooperation with companies.
• Acquire knowledge about Design Thinking, agile project management and independent execution of projects in teamwork.
• Apply interdisciplinary knowledge, integrate the problem poser into the project in an agile manner and present the results of their work.

Througout the course students:

• gain insights into the theory and applications of Deep Learning.
• are able to understand and explain basic terminology and assess which problems deep learning is particularly well suited for, and know about disadvantages/difficulties
• gain first experiences in important current technologies in the field of Deep Learning and gain insights into important application areas.
• are able to implement selected methods in Python with the help of suitable Deep Learning frameworks

Teaching content:

• Backpropagation and deep neural network training
• Automatic differentiation
• Initialization and regulariziation
• Deep Learning for computer vision with CNNs (image classification, object detection, segmentation)
• Recurrent Neural Networks and LSTMs
• Attention Mechanisms and Transformer Models
• Generative Models (VAEs, GANs, Diffusion Models)

The students obtain insights into the inner workings of modern 3D game engine and their applications. They learn the most important mechanism behind 3D game engines and modern methods to design and implement interactive 3D applications.

Topics include:

Mathematical basics: vector spaces, homogeneous coordinates, coordinate transformations and projections

 3D graphics: scene graphs, camera, rendering 3D objects, textures and uv-coordinates, light and shadow, visibility

 Collision detection, basics of 3D physics engines

  3D graphics in-depth: graphics pipeline, light models, BRDFs, vertex and pixel shader

 AI: way finding, decision making

 Application development: handling events and states, design patterns

Students are motivated to work scientifically and will be able to acquire subject-specific knowledge from the scientific literature and to prepare this knowledge for specific target groups.

The course covers four areas:

1. Basics of scientific work

Students understand the necessity of a scientific approach to problems and are able to understand the basic concepts of scientific work. (e.g. research questions, argumentation logic, writing style, citation)

2. Research methods

Students are able to apply essential research methods commonly used in business informatics and to assess the basic applicability of these methods for a given problem. In addition, they understand the basics of design-oriented research (Design Science). Furthermore, they are able to conduct a systematic literature study on their own.

3. Handling of scientific texts

Students can describe the structure of scientific texts, apply reading strategies and assess their basic scientific quality. Furthermore, they can compile, evaluate and compare the core statements of different scientific texts.

4. Presentation and discussion

In the area of presentation and discussion, students understand the essential elements of effective presentations and are able to apply them to a lecture. In addition, they are able to apply argumentation strategies for professional discussions and methods for effective discussion moderation.

Implicitly, this course promotes the English language level of the students to the level B2.2/C1.1 of the CEFR. Through intensive literature work with English-language scientific texts and their presentation/discussion, they have the ability to understand the main content of complex texts on concrete and abstract topics as well as to participate in specialist discussions in the field of business information systems.