INFO 4271 - Modern Search Engines

Search engines are the main interface between people and humankind's massive globally distributed repositories of knowledge. In this practice-focused course, we will review information retrieval basics such as web crawling, content indexing, index compression, query processing, and result ranking, before moving on to advanced techniques for personalization, (dense) neural retrieval, and stochastic ranking. The capstone to this class will be a practical project in which students design and build their own search engines that will be evaluated in a retrieval competition.

INFO 4193 - Natural Language Processing

Natural Language Processing (NLP) is a sub-field of artificial intelligence that aims at understanding and automatic generation of texts for various applications, such as document classification, sentiment analysis, text summarization, speech recognition, etc. This course covers NLP topics including n-gram models, word embeddings, bag of word representations for document classification, classifiers, tokenization, part of speech tagging, matrix factorization and topic modeling, deep learning for language processing, transformers, language models and text generation, and finally applications such as document summarization, machine translation, or question answering.

Understanding Large Language Models

We will look (among others) at LLM architecture, training & fine-tuning, prompting, mechanistic interpretability of LLMs, LLM agents and different evaluation methods. Participants will be offered both a technical perspective and encouraged to critically think about important topics relevant to cognitive science and society in the context of LLMs.

S00VPSM01 - AI in Biomedicine (Open Lecture)

In five sessions throughout the summer semester, this open lecture series will introduce the basic principles underlying the AI craze and its applicability in medicine. We will begin with general machine learning and study design considerations, cover computer vision and natural language processing, and conclude with a discussion on ethics and accountability.

Introduction to Artificial Intelligence

This course will teach the fundamental theory and methods of artificial intelligence (AI). It will give a representative overview of traditional methods as well as modern developments in the areas of (deep) machine learning, natural language processing and information retrieval. The course is designed to be accessible to non-computer science students and will only require basic prior programming experience. A primer on Python programming will be covered as part of the practical exercises. The course will be accompanied by practical assignments applying the discussed techniques. Understanding of formal theoretical knowledge will be assessed in a final exam.

Introduction to Python Programming

This course will give a practical introduction into programmatic thinking, algorithms and problem solving using the Python programming language as a vehicle.