Computational interaction with Bayesian methods

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

doi:10.1145/3290607.3298820

Computational interaction with Bayesian methods

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Abstract › Scientific › vertaisarvioitu

4 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Sivumäärä	6
DOI - pysyväislinkit	https://doi.org/10.1145/3290607.3298820
Tila	Julkaistu - 2 toukok. 2019
OKM-julkaisutyyppi	Ei sovellu
Tapahtuma	ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Glasgow, Iso-Britannia Kesto: 4 toukok. 2019 → 9 toukok. 2019 https://chi2019.acm.org/

Conference

Conference	ACM SIGCHI Annual Conference on Human Factors in Computing Systems
Lyhennettä	ACM CHI
Maa/Alue	Iso-Britannia
Kaupunki	Glasgow
Ajanjakso	04/05/2019 → 09/05/2019
www-osoite	https://chi2019.acm.org/

Pääsy asiakirjaan

10.1145/3290607.3298820

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Link to publication in Scopus

Siteeraa tätä

@conference{f2907a00043d4f97802188342d63d314,

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.",

keywords = "Computational interaction, Inference, Machine learning, Optimization",

author = "Kristensson, {Per Ola} and Antti Oulasvirta and Nikola Banovic and John Williamson",

year = "2019",

month = may,

day = "2",

doi = "10.1145/3290607.3298820",

language = "English",

note = "ACM SIGCHI Annual Conference on Human Factors in Computing Systems, ACM CHI ; Conference date: 04-05-2019 Through 09-05-2019",

url = "https://chi2019.acm.org/",

}

TY - CONF

T1 - Computational interaction with Bayesian methods

AU - Kristensson, Per Ola

AU - Oulasvirta, Antti

AU - Banovic, Nikola

AU - Williamson, John

PY - 2019/5/2

Y1 - 2019/5/2

N2 - 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.

AB - 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.

KW - Computational interaction

KW - Inference

KW - Machine learning

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=85067307646&partnerID=8YFLogxK

U2 - 10.1145/3290607.3298820

DO - 10.1145/3290607.3298820

M3 - Abstract

AN - SCOPUS:85067307646

T2 - ACM SIGCHI Annual Conference on Human Factors in Computing Systems

Y2 - 4 May 2019 through 9 May 2019

ER -

Computational interaction with Bayesian methods

Abstrakti

Conference

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

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