Recommended prerequisite for participation in the module

Bachelor of Engineering or Bachelor of Science (BSc) in Engineering within Management Engineering, Business Systems or the like.

Content, progress and pedagogy of the module

This module is designed for professionals and researchers who wish to explore and leverage the transformative potential of Large Language Models (LLMs) in industrial applications.

1. LLM Fundamentals: Gain deep insights into the architecture, function, and development of large language models, including their training methods, capabilities, and state-of-the-art technologies for complex language tasks. Explore both commercial and open-source LLM tools and frameworks.
    
2. Practical Implementation: Learn the critical processes of choosing appropriate models and preparing datasets for specific industrial applications. Acquire hands-on techniques for integrating LLMs into existing industrial processes, including training, fine-tuning, and optimization methods.
    
3. Industrial Applications of NLP and LLMs: Discover how LLMs can be used to solve complex problems across various industrial sectors, focusing on text generation, analysis, and other NLP tasks.
    
4. Ethical and Responsible AI: Discuss the ethical implications, challenges, and best practices for implementing LLMs in an industrial context.
    
5. Future Trends and Innovations: Stay ahead of the curve by exploring the latest advancements and future directions in the field of LLMs and their industrial applications.

Learning objectives

Knowledge

• Understand the fundamental principles and architecture of Large Language Models.
   
• Recognize the potential applications of LLMs across various industrial sectors.
   
• Identify current trends and future directions in LLM technology and its industrial applications.

Skills

• Select appropriate LLM models and prepare datasets for specific industrial applications.
   
• Apply practical techniques for training and fine-tuning LLMs to solve industrial challenges.
   
• Utilize commercial and open-source LLM tools and frameworks effectively.
   
• Implement LLMs to optimize industrial processes and drive innovation.
   
• Analyze and evaluate the performance of LLMs in various industrial contexts.

Competences

• Independently assess and select suitable LLM solutions for complex industrial problems.
   
• Design and implement LLM-based systems to address specific industrial needs.
   
• Evaluate the ethical implications and potential risks of LLM implementations in industry.
   
• Continuously adapt to and incorporate new developments in LLM technology within industrial applications.

Exam

Exams

Name of exam	Advanced Applications of Large Language Models for generative AI in Industry
Type of exam	Active participation/continuous evaluation Reexamination is conducted as a replacement assignment.
ECTS	5
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures

Additional information

Modulet udbydes som et særskilt modul.