Computational interaction with Bayesian methods

Research output: Contribution to conferenceAbstractScientificpeer-review


Research units

  • University of Cambridge
  • University of Michigan, Ann Arbor
  • University of Glasgow


This course introduces computational methods in human-computer interaction. Computational interaction methods use computational thinking-abstraction, automation, and analysis-to explain and enhance interaction. This course introduces the theory of practice of computational interaction by teaching Bayesian methods for interaction across four wide areas of interest when designing computationally-driven user interfaces: decoding, adaptation, learning and optimization. The lectures center on hands-on Python programming interleaved with theory and practical examples grounded in problems of wide interest in human-computer interaction.


Original languageEnglish
Number of pages6
Publication statusPublished - 2 May 2019
MoE publication typeNot Eligible
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Glasgow, United Kingdom
Duration: 4 May 20199 May 2019


ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
CountryUnited Kingdom
Internet address

    Research areas

  • Computational interaction, Inference, Machine learning, Optimization

ID: 35822914