This course provides a technical perspective on NLP - methods for building computer software that understands and manipulates human language. Contemporary data-driven approaches are emphasized, focusing on machine learning techniques. The covered applications vary in complexity, including for example Entity Recognition, Argument Mining, or Emotion Analysis.

Large Language Models (LLMs), such as GPT-3, BERT, and their successors, have had an enormous impact on various domains, including natural language processing, machine learning, and artificial intelligence. These models have redefined what’s possible in applications such as text generation, translation, summarization, sentiment analysis, and more. The aim of this seminar is to explore the cutting-edge research, insights, and trends in the field of LLMs.

The NLP Lab course provides students with a detailed look at the recent advancements in NLP, covering various aspects such as large language models (LLMs), conversational systems, and computational social science.

The seminar aims to introduce students to the ethical dilemmas of artificial intelligence. Students will develop skills in assessing AI systems, identifying ethical dilemmas and social impacts, reasoning through ethical and social issues, and communicating their reasoning.

Data science aims at making sense of big data. To that end, various tools have to be understood to help in analyzing the arising structures. Often data comes as a collection of vectors with a large number of components. Understanding their common structure is the first main objective of understanding the data. The geometry and the linear algebra behind them become relevant and enlightening.

This course is a detailed introduction to the architecture of conversational systems (chatbots). We will introduce the main components of dialog systems and show approaches to their implementation, including natural language understanding, natural language generation, and dialog sequence management.