The goal of the summer school is to teach the new generation of HCI researchers the foundations of computational methods for user interface design and evaluation. This encompasses modeling of interaction (including deriving and engaging with statistical models), automatic learning of preferences and computer-assisted optimization of interfaces. Applied machine learning and appropriate quantitative analysis, suitable for real-time, closed-loop interactions remain key elements of the summer school program.
Traditionally, the school places strong focus on developing applied skills through practical sessions integrated into the school program, which gives students practical experience in using well-grounded, cutting edge analysis, modeling and inference in engineering interactive systems.
This is the seventh installment of the summer school and part of an annual series: