Applications of Artificial Intelligence

Faculty

Faculty of Agricultural Science and Landscape Architecture

Version

Version 1 of 17.07.2025.

Module identifier

44M0509

Module level

Master

Language of instruction

English

ECTS credit points and grading

5.0

Module frequency

only winter term

Duration

1 semester

 

 

Brief description

In this seminar, students learn to understand the transformative power of AI across various domains. This course demystifies how AI operates and its role in solving complex, real-world problems. By exploring a range of AI use cases, students will gain insights into the technology's potential, limitations, and the ethical and legal considerations that accompany its implementation. Through collaborative hands-on work, exploration and group presentations, this seminar not only deepens the understanding of AI's mechanics but also encourages critical thinking about its implications in our rapidly evolving world. This module is tailored for those who seek to grasp the essence of AI's influence in their respective fields, preparing them for a future where AI's presence is increasingly significant.

Teaching and learning outcomes

Definition, overview, and classification of AI

Fundamentals of machine learning and neural networks

AI use cases in various domains

Current developments in the field of AI

Potential paths for the future of AI

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
25SeminarPresence-
Lecturer independent learning
Workload hoursType of teachingMedia implementationConcretization
25Preparation/follow-up for course work-
50Work in small groups-
25Study of literature-
25Presentation preparation-
Graded examination
  • Oral presentation, with written elaboration or
  • Written examination or
  • oral exam
Ungraded exam
  • Regular participation
Remark on the assessment methods

The standard examination form is a presentation/report; deviations from this will be announced in the first four weeks after the start of lectures.

Ungraded: Regular participation in the seminars

Exam duration and scope

presentation/report: ca. 20–30-minute presentation with 5–10 page written analysis

Recommended prior knowledge

As prerequisites for this seminar, students are encouraged to embrace an open-minded attitude towards learning new concepts and should not hesitate to engage with technical and IT-related content, as this will be crucial for a comprehensive understanding of the applications of artificial intelligence.

Knowledge Broadening

Students can explain the term artificial intelligence, recognizing its multidisciplinary nature and citing definitions from different perspectives

Students are familiar with key sub-fields of artificial intelligence, understanding their distinct roles and applications.

Students can identify and differentiate between various machine learning strategies, such as supervised, unsupervised, and reinforcement learning, understanding their unique methodologies and use cases.

Students can articulate examples of AI applications within their field of study, illustrating how AI solves specific problems and discussing the potential impact.

Students explain the role of AI prediction in the decision-making process.

Application and Transfer

Students utilize available AI tools and interfaces to build, evaluate, and refine solutions for selected use cases, focusing on understanding the tool's capabilities and limitations.

Students apply effective prompting techniques and other interaction strategies to utilize AI models efficiently, learning to tailor inputs for optimal outputs.

Students apply AI concepts to design new solutions to real-world problems in their domain, demonstrating the ability to translate theoretical knowledge into practical solutions.

Communication and Cooperation

Students develop skills in collaborative project work, including research, analysis, and presentation, focusing on AI-related topics.

Literature

The list of recommended literature for the seminar will be provided at the beginning of the semester, ensuring that the most current and relevant resources are included for your study and reference.

Applicability in study programs

  • Land Use Transformation
    • Land Use Transformation M.Sc. (01.03.2026)

  • Applied Livestock Sciences
    • Applied Livestock Sciences M.Sc. (01.09.2025)

  • Agriculture, Food Science and Business
    • Agriculture, Food Science and Business M.Sc. (01.09.2025)

  • Applied Plant Sciences M.Sc. (01.09.2025)
    • Applied Plant Sciences M.Sc. (01.09.2025)

    Person responsible for the module
    • Meseth, Nicolas
    Teachers
    • Meseth, Nicolas