ACM SIGCHI 2019 Course on Bayesian Methods for Interaction

Course on Computational Interaction with Bayesian Methods

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

• 0900 - 1720 Wednesday 8 May 2019, Glasgow, UK

See the course website for full details

Launch the notebooks on Binder

No need to install anything: thanks to the amazing Binder service, you can open and run these notebooks directly on the web. Just click the link above to launch a VM.

Notebooks

• 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/

Topic

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.

Instructors

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

Local install instructions

If you are not using mybinder.org, then you can download and install a local version:

• Install Anaconda 3.6 for your platform if you don’t already have it installed (note Python 3.7 currently has a conflict with gpyopt)

• Clone the repository somewhere on your machine

    git clone https://github.com/johnhw/chi_course_2019.git
  

• At the terminal, enter the directory where you cloned the repo

• Create a new conda environment with the prerequisites

    conda env create -f environment.yml
  

• Activate the environment with

    conda activate chi-course-2019
  

• Start the notebook server with

  jupyter notebook
  

• and then open index.ipynb