Name
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3290607.3298820 | CHI Extended Abstracts |
Keywords | Field | DocType |
computational interaction, inference, machine learning, optimization | Inference,Computer science,Automation,Human–computer interaction,Decoding methods,User interface,Practice theory,Python (programming language),Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-1-4503-5971-9 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Per Ola Kristensson | 1 | 1317 | 91.21 |
| Nikola Banovic | 2 | 32 | 4.64 |
| Antti Oulasvirta | 3 | 3131 | 217.78 |
| John Williamson | 4 | 209 | 20.23 |