Computational interaction with Bayesian methods

Per Ola Kristensson; Antti Oulasvirta; Nikola Banovic; John Williamson

doi:10.1145/3290607.3298820

Abstract

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 language	English
Number of pages	6
DOIs	https://doi.org/10.1145/3290607.3298820
Publication status	Published - 2 May 2019
MoE publication type	Not Eligible
Event	ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Glasgow, United Kingdom Duration: 4 May 2019 → 9 May 2019 https://chi2019.acm.org/

Conference

Conference	ACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated title	ACM CHI
Country	United Kingdom
City	Glasgow
Period	04/05/2019 → 09/05/2019
Internet address	https://chi2019.acm.org/

Keywords

Computational interaction
Inference
Machine learning
Optimization

Access to Document

10.1145/3290607.3298820

Link to publication in Scopus

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Cite this

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title = "Computational interaction with Bayesian methods",

abstract = "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.",

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AU - Banovic, Nikola

AU - Williamson, John

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KW - Machine learning

KW - Optimization

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