Behind the Secrets of Large Language Models (WiSe 26/27)

TU Dresden | Winter semester 2026 / 2027 Behind the Secrets of Large Language Models

This course provides a practical and in-depth understanding of large language models that power modern natural language processing systems. Students will explore the architecture, training methodologies, capabilities, and ethical implications of LLMs. The course combines theoretical knowledge with hands-on experience to equip students with the skills necessary to develop, analyze, and apply LLMs in various contexts. 

Lecture by Michael Färber and Simon Razniewski, winter term 2026/27. 

 

 

THE SCHEDULE IS NOT YET ANNOUNCED, below is the schedule from last year.

 

  • Lecture: Mondays, 11:10-12:40, FOE/244
  • Lab: Mondays, 16:40-18:10, FOE/244
  •  

The course is divided into three parts:

  1. Foundations (lectures 1-4)
  2. Building and training LLMs (lectures 5-9)
  3. Using and extending LLMs (lectures 10-15)

Tentative schedule:

# Date Lecture Lab
1 13.10. Intro (Razniewski) Crash course
2 20.10. Word representation (Razniewski) Word representation
3 27.10. Neural networks (Färber) Neural networks
4 03.11. Deep Learning+Attention (Färber) Attention
5 10.11. Training data (Razniewski) Training data
6 17.11. Architectures (Färber) Architectures
7 24.11. Training (Razniewski) Pre-training
8 01.12. Transfer learning (Färber) Fine-tuning
9 08.12. Evaluation (Razniewski) Evaluation
10 15.12. Applications (Färber) Project work
11 05.01. RAG (Razniewski)  
12 12.01. Vision LMs (Haase)  
13 19.01. Agents I (Haase)  
14 26.01. Agents II (TBD)  
15 02.02. Ethics and safety (Razniewski)  

 
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