Large Language Models and Agents (HWS2024)

Large language models (LLMs) such as GPT, Llama, Gemini, and Mixtral have the potential to enable a wide range of new applications and to significantly improve the performance of existing systems. The course introduces students to LLMs and teaches them how to employ the models within applications.

The course covers the following topics:

  1. Introduction to LLMs
  2. Prompt engineering patterns
  3. LLM-based agents
  4. Evaluation of LLMs and agents
  5. Development of LLM-based applications

The course participants gain knowledge about the principles of training LLMs. They will be able to identify opportunities for employing LLMs in business applications and will learn to apply prompt engineering techniques as well as agent frameworks for solving complex tasks. In the second half of the course, the participants apply their knowledge in team projects and will report about the results of the projects in the form of a written report as well as an oral presentation.

Admission (HWS 2024)

IMPORTANT: The number of participants for this course is limited. The admission for this course requires two active steps from the side of the students:

  1. Please register for the course in Portal2 as for any other course until 2nd of September 2024
  2. As an additional requirement for admission, please send an up-to-date Transcript of Records to Ralph Peeters by 28th of August 2024 (end of day). Transcripts should be in English or German. If you have not yet completed at least 5 courses at the University of Mannheim, please send us the Transcript of your previous study program. If the grading system differs from the German one, please attach a relevant description.

 We will inform you if you are admitted to the course early in the lecture period (first September week).

Time and Location

  • Thursdays, 15:30–17:00. Location: A5 C015 (Starting: 05.09.2024)

Instructors

Assessment

In the second half of the course, students will work on applied projects in teams. The grade received for this course is based on the quality of the contents of their project report (70%) and the associated presentation (30%).

There is no dedicated written or oral exam for this course.

ECTS

  • 3 ECTS: 70 % project report, 30 % presentation

Requirements

  • Programming skills in Python
  • Basic machine learning concepts and techniques

Registration and Participation

  • The course is open to students of the Mannheim Master in Data Science, the Mannheim Master in Social Data Science, and Master Business Informatics.
  • The course is restricted to 30 participants. The registration for the course is organized by the Studiengang Management and is done via Portal2. 

Literature