Fundamentals of People Oriented Computing

Fundamentals of People-Oriented Computing is a new teaching format that replaced the "Master Basismodule".

It is part of the People-Oriented Computing (POC) study direction, comprising the topics offered by IFI that focus on the relationship between humans and technology. POC deals with the human-centered design of information technology. User-friendliness, security, and cultural and ethical aspects are of core importance. You create interactive systems and visualize information.

Several professors will offer topics to choose from. The topics we are offering run under the umbrella "Human-Centered Interactive Visual Data Analysis".

The Fundamentals of POC is designed to:

• Provide a deep dive into a POC topic that excites you
• Give you experience in reading and analyzing scientific literature
• Give you the chance to gain familiarity with a professor or research group in POC
• Complement other lectures and teaching formats with a self-directed module

Basic format of topic is reading scientific literature and individual oral examination

• Reading is typically 250-300 pages, depending on format
• Course grade is mainly determined by the grade of the assessment

This topic investigates the human-centered component in data-centric research and practice. We will study concepts, methodologies, and techniques focusing on the Human-Centered AI, Human-Centered Machine Learning, and Human-Centered Data Analysis in particular.

This year, we have ten focus topics at your disposal. Focus topics are specializations of the human-centered IVDA topic, towards an aspect that is most interesting to you:

• XAI for Recommender Systems: interactive visual explanations for recommender systems
• IVDA and Data Humanism: how principles of data humanism align with interactive visual data analysis
• Human-AI Teaming: discover how interactive visual data analysis can facilitate human and machine teaming
• Human-Centered Healthcare: how interactive visual data analysis in personal and digital health
• Item Ranking: ranking items by more than one attribute at the same time
• Item Labeling: human-centered ways to create  training data for supervised machine learning
• Digital Libraries and Humanities: how interactive visual data analysis techniques can help
• Sustainability: applying interactive visual data analysis techniques to make the world more sustainable
• Item Similarity, Search and Exploration: human-centered forms of similarity definition, search, and exploration
• Human-Centered Relation Discovery: interactive visual techniques to find new (co-)relations in multivariate datasets