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Hochschule Osnabrück - University of Applied Sciences

Basics of Artificial Intelligence

Faculty

Faculty of Engineering and Computer Science

Version

Version 1 of 03.12.2025.

Module identifier

11B2011

Module level

Bachelor

Language of instruction

German

ECTS credit points and grading

5.0

Module frequency

winter and summer term

Duration

1 semester

 

 

Brief description

In a rapidly developing technological environment, basic theoretical knowledge and practical skills in the field of artificial intelligence (AI) are of great importance for technical professions. The application of AI in various fields of application not only promotes innovation but is also a driving factor for progress in science and business. This module provides a sound introduction to AI, clearly focusing on basic theoretical knowledge and practice-oriented skills. This knowledge and skills lay the foundation for the independent learning of new AI concepts of the future and the independent application of AI in different fields. 

Teaching and learning outcomes

The topic of AI in particular is currently undergoing a major change in terms of content. The content is therefore constantly being adapted, so the list below is not exhaustive.

  1. Introduction to artificial intelligence (AI)
  2. Machine learning as a sub-area of AI
  3. Process of AI development
  4. Classes of learning methods
    1. supverized (supervised) learning methods
    2. unsupverized (unsupervised) learning methods
    3. semi-supverized learning methods
    4. self-supervised learning methods
    5. online and batch learning
  5. Other current learning methods, e.g.
    1. Transformer / generative AI
    2. attention networks
    3. Generative adversarial networks
    4. Learning methods and algorithms
  6. AI development and AI development environments
  7. Use cases and exercises
  8. Selected topics in AI, e.g.  
    1. Recommender systems
    2. Process mining
    3. Image understanding
    4. Time series analysis
    5. Web mining
    6. Distributed learning methods
  9. Deployment of AI models
  10. Introduction to the use of high-performance computing clusters

Overall workload

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

Teaching and learning methods
Lecturer based learning
Workload hoursType of teachingMedia implementationConcretization
30LecturePresence or online-
30PracticePresence or online-
Lecturer independent learning
Workload hoursType of teachingMedia implementationConcretization
40Preparation/follow-up for course work-
30Creation of examinations-
20Study of literature-
Graded examination
  • oral exam or
  • Homework / Assignment
Ungraded exam
  • Field work / Experimental work or
  • Regular participation
Remark on the assessment methods

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.

Exam duration and scope

Oral examination - see study regulations

Term paper - approx 15 pages, rpresentation approx. 10 minutes

Recommended prior knowledge

Students will gain in-depth knowledge of programming as well as knowledge of mathematics (esp. Linear Algebra, Analysis). students, as they are acquired in the introductory modules.

Students who would like to refresh their knowledge and skills before starting the module are recommended to read  literature:

Programming: 

Python - Learn to Programming Step by Step, Brunner, 2023

Python 3: The Comprehensive Manual

Mathmatic: 

Basic literature covering linear algebra and analysis

Knowledge Broadening

Graduates have basic knowledge in the field of weak AI. They are able to carry out simple AI development tasks under supervision, also using HPC computing.

Knowledge deepening

Students are enabled and guided to independently deepen their knowledge and practical skills in selected topics (e.g. special algorithms).

Knowledge Understanding

Students can evaluate and critically reflect on the applicability of AI development approaches and algorithms. 

Application and Transfer

Students can transfer the content and concepts they have learned to new tasks. 

Communication and Cooperation

Graduates are able to present their work results orally and in writing in a clearly structured form and discuss them with team members, “virtual” clients and technical experts.

Literature

Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und Tensorflow, O'Reilly, aktuellste Auflage (aktuell: 3. Auflage, 2023)

Han, Kamber: Data Mining Concepts and Techniques

Witten, Frank: Data Mining (Forth Edition)

Kotu, Deshpande: Predictive Analytics and Data Mining

Russel, Norvic: Artificial Intelligence: A Modern Approach​

Applicability in study programs

  • Computer Science and Media Applications

    • Computer Science and Media Applications B.Sc. (01.09.2025)

  • Agricultural Technologies

    • Agricultural Technologies B.Sc. (01.09.2025)

  • Computer Science and Computer Engineering

    • Computer Science and Computer Engineering B.Sc. (01.09.2025)

    Person responsible for the module
    • Tapken, Heiko
    Teachers
    • Tapken, Heiko
    • Stiene, Stefan
    • Schöning, Julius