IE 696 Advanced Methods in Text Analytics

This course is a follow-up to IE 661 Text Analytics, where basic concepts and methods for natural languange processing (NLP) are introduced. In this course, we dive deep into the latest state-of-the-art methods for NLP. This means this course is heavily focused on language models implemented using deep learning architectures. Among other things, we will take a close look at the transformer architecture and its applications, including the design, training and applications of large language models (LLMs), the technology behind products like ChatGPT. Much of the fundamentals of the course are additionally supported with hands-on coding exercises using Python.

Time and Location

• Lectures: Tuesdays 13:45 – 15:15 in A5 6 Room C015
• Tutorials: Wednesdays 08:30–10:00 in A5 6 Room C015 (starts on 2nd week, February 19th)

Grading / Evaluation (Tentative)

• 100% final exam

Instructors

• Dr. Daniel Ruffinelli
• Prof. Dr. Simone Paolo Ponzetto

Attendance

• The course is open to students of the Master Business Informatics, Mannheim Master in Data Science (MMDS) and Mannheim Master in Business Research (MMBR) Information Systems.

Course materials and up-to-date information can be found in our ILIAS group.

Most of the advanced material comes from relatively recent research papers referenced in the course materials, but the basics are based on the following textbooks:

• Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Dan Jurafsky and James H. Martin, (3rd ed.), 2023.
• Natural Language Processing, Yue Zhang and Zhiyang Teng, 2021.
• Natural Language Processing, Jacob Eisenstein, 2018.