Presentation by Holger Hoos, Turing building (X campus), 20 February 14:00
Machine Learning & Optimisation : Promise and Power of Data-driven, Automated Algorithm Design
by Holger Hoos, University of British Columbia
Computational tools are transforming the way we live, work and interact ; they also hold the key for meeting many of the challenges that we face as individuals, communities and societies. Machine learning and optimisation techniques play a particularly important role in this context, and cleverly combined, they can revolutionise the way we solve challenging computational problems - as I will demonstrate in this talk, using examples from mixed integer programming, planning and propositional satisfiability, as well as from prominent supervised machine learning tasks. The fundamental techniques I will cover include automated algorithm selection, configuration and hyperparameter optimisation, as well as performance prediction and Bayesian optimisation. I will also motivate and explain the Programming by Optimisation paradigm for automated algorithm design, which further extends the reach of those techniques.
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