Principle Component Analysis (PCA) is used by many but understood by few.
In this talk I hope to explain some of the math behind the tool, to raise awareness for its potential (and pitfalls) and to discuss the relationship between PCA and cluster analysis. In addition I will present some examples of its use in neuro-science, from my own work in robotics and an application in economics.
Date: 21/10/2016
Time: 16:00
Location: LB252
neuroscience Computer Science COVID-19 PCA Persistent homology Convolutional Neural Networks Deep learning Associative memory Purkinje cell cerebellum olfaction computational neuroscience Neuromorphic hardware Dendritic computation Evolutionary algorithms Software development optimization Open position Structural plasticity neural networks robotics Neurons Studentship Neuronal Morphology Artificial intelligence computational modelling Homoeostasis Network connectivity synaptic plasticity machine learning