- 0900 - 1720 Wednesday 8 May 2019, Glasgow, UK
See the course website for full details
- 0900-1020 01_intro_to_bayesian_methods/ Introduction to Bayesian methods in HCI and Bayesian filtering to estimate state
- 1100-1220 02_decoding_symbols/
- 1400-1520 03_bayesian_optimisation/
- 1600-1720 04_modeling_behavior/
The course focuses on optimization and inference and on applying these techniques to concrete HCI problems. The course will specifically look at Bayesian methods for solving decoding, adaptation, learning and optimization problems in HCI. The lectures center on hands-on Python programming interleaved with theory and practical examples grounded in problems of wide interest in human-computer interaction.
The following faculty members will teach the course:
- Nikola Banovic, University of Michigan, USA
- Per Ola Kristensson, University of Cambridge, UK
- Antti Oulasvirta, Aalto University, Finland
- John Williamson, University of Glasgow, UK
If you are not using mybinder.org, then you can download and install a local version:
-
Install Anaconda 3.7 for your platform if you don't already have it installed
-
Clone the repository somewhere on your machine
git clone https://github.com/johnhw/chi_course_2019.git -
At the terminal, create a new conda environment and activate it with
conda create -n chi-course-2019 conda activate chi-course-2019 -
Enter the directory where you cloned the repo
-
Install the prerequisites with
conda env export --no-builds -f environment.yml -
Start the notebook server with
jupyter notebook -
and then open
index.ipynb