University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Behavioural Machine Learning

Behavioural Machine Learning

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Hector Basevi.

Host: Dr. Ata Kaban

Speaker’s website:

Abstract: In recent years, decision theory and behavioural science moves more and more in the direction of using large and complex datasets. While there are many empirical studies which apply decision-theoretic methodology to large field datasets, the literature on theoretical and methodological synergies between decision theory/behavioural science and computer science is very scarce. This talk will explore whether and to what extent decision-theoretic modelling can enhance machine learning algorithms. Specifically, I will consider models of imprecision and noise as well as attribute-based modelling from decision theory and present several examples detailing how these tools can be merged with existing machine learning algorithms to generate new insights into human behaviour. Comparing predictive and explanatory power of machine learning versus decision-theoretic modelling approaches, I will show examples when the two approaches outperform each other as well as when they can be combined producing powerful synergies.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


Talks@bham, University of Birmingham. Contact Us | Help and Documentation | Privacy and Publicity.
talks@bham is based on from the University of Cambridge.