School of Computer Science
Selected PresentationsFor paper presentations, go to the publications page.
Learning from Imbalanced Classes: Problem Statement & Methods
Talk contributed to Workshop on Class Imbalance in Machine Learning Classification, organized by the 4IR STFC Centre for Doctoral Training in Data Intensive Science of the University of Manchester, UK.
Boosting for Probability Estimation & Cost-Sensitive Learning
Lecture on the work I carried out during my PhD, along with online extensions and connections to other areas of machine learning I am currently exploring. This is the full version of the talk (~ 1.5h). Appropriately adapted, shorter versions were delivered in:
Asymmetric boosting algorithms: Do we really need them?
Lecture on my latest research delivered in the Research Symposium of the University of Manchester, UK.
Cost-sensitive learning with AdaBoost
A brief introduction to cost-sensitive learning, followed by my latest research on cost-sensitive AdaBoost, delivered to the postgraduate class of COMP61011: Foundations of Machine Learning of the University of Manchester, UK.
Optimal inductive inference and its approximations
Two-part talk on inductive inference delivered to the members of the Machine Learning and Optimization research group of the University of Manchester, UK. We first gave an intuitive interpretation of Solomonoff Induction, an intractable formalization of optimal inductive inference which combines ideas from philosophy, computer science, statistics & information theory. We then saw how nature and machine learning overcome the ill-posedness of induction by introducing assumptions and settling for approximations. Concepts covered included: common assumptions, bias-variance tradeoff, inductive bias & the no-free-lunch theorems, the role of Occam's Razor and elements of statistical learning theory.
Introduction to AdaBoost
Introductory lecture on AdaBoost delivered to the postgraduate class of COMP61011: Foundations of Machine Learning of the University of Manchester, UK.