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
neural networks Associative memory Studentship cerebellum Artificial intelligence olfaction Dendritic computation Purkinje cell Neuronal Morphology Evolutionary algorithms Neuromorphic hardware Persistent homology Open position COVID-19 Network connectivity robotics Deep learning Convolutional Neural Networks Neurons computational neuroscience neuroscience computational modelling Homoeostasis Software development machine learning PCA Computer Science optimization Structural plasticity synaptic plasticity