Advanced AI Technologies and Applications

11M2009

Master

German

5.0

only summer term

1 semester

Hardly any other topic is advancing as quickly and changing many areas of the professional world as much as artificial intelligence. National and international scientific communities (e.g. SIGKDD, ACM KI, GI FB-KI, etc.) and research projects (including Osnabrück University of Applied Sciences) contribute to making research results available within and outside scientific associations in the form of publications, open source/open data positions.  The Advanced AI Technologies and Applications module teaches students advanced concepts of artificial intelligence with a focus on machine learning. Current, scientifically relevant developments in the field of AI are addressed and discussed. The module not only deals with the theory of AI development, but also addresses advanced concepts and tools (e.g. data and model management) that are necessary for the development and practical application of AI.

1. overview: AI, modeling and machine learning

2. advanced architectures of neural networks

3. advanced concepts of AI development (e.g. MLOps)

4. tools and frameworks for AI development

5. current developments in the field of AI

6. testing new technological approaches in the context of working on a research question

The total workload for the module is 150 hours (see also "ECTS credit points and grading").

• Homework / Assignment

• Field work / Experimental work or
• Regular participation

The selection of graded and ungraded examination types from the given options is the responsibility of the respective teacher. They must adhere to the applicable study regulations.

Graded examination component:

• Term paper – approx. 15 pages, accompanying presentation approx. 10 minutes

Non-graded examination component:

• Experimental work: Experiment: approx. 5 experiments in total
• Regular attendance: Attendance of at least 80% of the course

This module requires knowledge from the module "Fundamentals of Artificial Intelligence" as well as practical programming experience. Students should also have basic knowledge of probability theory and advanced knowledge of linear algebra and analysis. The focus of the course is on scientifically sound, yet application-oriented teaching, so that the application of mathematical knowledge is paramount.

Students who would like to refresh their knowledge and skills before the start of the module are advised to contact the lecturer in order to obtain access to appropriate preparation material adapted to the semester. 

The following literature is recommended: 

- Practical introduction to machine learning with Scikit-Learn, Keras and TensorFlow

Students who have successfully studied this subject are familiar with advanced concepts, methods, architectures and processes of AI development and can apply them in complex projects.

Students deepen their knowledge of selected aspects of AI development with the aid of current research results.

Graduates are familiar with advanced concepts, algorithms and methods of AI development. They are able to apply these to complex tasks, also using a high-performance cluster. 

Students who have successfully completed this module will be able to independently familiarize themselves with new technologies in the field of AI. This includes both the development of AI-based solutions and the application/inference of AI models or solutions. 

Graduates explain research results and interpret them critically. The above-mentioned research question ideally originates from a research project of the teacher.

Students are able to critically assess the use of knowledge-based algorithms and can communicate these with users and developers in a subject-related context. They integrate participants into tasks in a goal-oriented manner, taking into account the respective group situation. 

• Stiene, Stefan

• Stiene, Stefan
• Gervens, Theodor
• Tapken, Heiko