Fall 2019 Postdoc Seminar Series: Special Guest Lecture - Ernest Fokoue
September 4, 2019 –
Talk Title: There is a Kernel Method for That
Speaker: Ernest Fokoue, Professor of Statistics, Rochester Institute of Technology
In this lecture, I will present a general tour of some of the most commonly used kernel methods in statistical machine learning and data mining. I will touch on elements of artificial neural networks and then highlight their intricate connections to some general purpose kernel methods like Gaussian process learning machines. I will also resurrect the famous universal approximation theorem and will most likely ignite a [controversial] debate around the theme: could it be that [shallow] networks like radial basis function networks or Gaussian processes are all we need for well-behaved functions? Do we really need many hidden layers as the hype around Deep Neural Network architectures seem to suggest or should we heed Ockham’s principle of parsimony, namely “Entities should not be multiplied beyond necessity.” (“Entia non sunt multiplicanda praeter necessitatem.”) I intend to spend the last 15 minutes of this lecture sharing my personal tips and suggestions with our precious postdoctoral fellows on how to make the most of their experience.