Machiene Learning and Application module open assessment
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Updated
May 9, 2017 - TeX
Machiene Learning and Application module open assessment
Open Assessment for Machine Learning and Applications module. This assessment scored 83% and was worth 8 credits of my third year.
This repository contains source code, data and paper submitted to HSI2020
Classifies time series data using ARMA modelling and kernel techniques on the Grassmann manifold
Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.
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