Interaction with Machine Learning
Principal lecturer: Prof Alan Blackwell
Additional lecturer: Dr Advait Sarkar
Taken by: Part II CST 75%
Hours: 16 (8 x 2-hour sessions)
Prerequisites: Further Human–Computer Interaction
Aims
This is an advanced course in human-computer interaction, with a specialist focus on intelligent user interfaces and interaction with machine-learning and artificial intelligence technologies. The format will be largely Practical, with students working in pairs to carry out a mini-project involving empirical research investigation. These studies will investigate human interaction with some kind of model-based system for planning, decision-making, automation etc. Possible study formats might include: System evaluation, Field observation, Hypothesis testing experiment, Design intervention or Corpus analysis, following set examples from recent research publications. Project work will be formally evaluated through a report and presentation.
Lectures
(note that Lectures 2-7 also include one hour class discussion
of practical work)
• Current research
themes in intelligent user interfaces
• Program
synthesis
• Mixed initiative
interaction
• Interpretability /
explainable AI
• Labelling as a
fundamental problem
• Machine learning
risks and bias
• Visualisation and
visual analytics
• Student research
presentations
Objectives
By the end of the course students should:
• be familiar with
current state of the art in intelligent interactive systems
• understand the human
factors that are most critical in the design of such systems
• be able to evaluate
evidence for and against the utility of novel systems
• have experience of
conducting user studies meeting the quality criteria of this
field
• be able to write up
and present user research in a professional manner
Recommended reading
Brad A. Myers and Richard McDaniel (2000). Demonstrational
Interfaces: Sometimes You Need a Little Intelligence, Sometimes
You Need a Lot.