teaching

SecretLLM course

SecretLLM: Behind the Secrets of Large Language Models

Period: Winter Semester 2024/2025 (planned for Winter Semester 2025/2026)

This course offers a practical and in-depth exploration of large language models (LLMs), which are at the core of modern natural language processing (NLP) systems. Students will gain hands-on experience alongside theoretical insights into LLM architecture, training methodologies, capabilities, and ethical considerations.

By the end of this course, students will be able to:

  1. Understand the architecture and key components of large language models.
  2. Analyze training processes, including data collection, model optimization, and fine-tuning.
  3. Evaluate the performance and limitations of LLMs across various NLP tasks.
  4. Apply LLMs to real-world applications, such as text generation, summarization, and translation.
  5. Discuss the ethical considerations and societal impacts of deploying LLMs.

Prerequisites

  • Prior knowledge of Introduction to Machine Learning or an equivalent course.
  • A basic understanding of neural networks.
  • Programming experience in Python.

This course is offered together with Prof. Simon Razniewski.