
The Shape of Data Geometry-Based Machine Learning and Data Analysis in R
by Farrelly, Colleen M.; Ulrich Gaba, YaƩ-
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Summary
The Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. Focused on practical applications rather than dense mathematical concepts, the book progresses through coding examples using social network data, text data, medical data, and education data. Readers will come away with an entirely new toolkit to use in their own machine-learning work, as well as with a solid understanding of some of the most exciting algorithms being used in the field today.
Author Biography
Yaé Ulrich Gaba completed his doctoral studies at the University of Cape Town (UCT, South Africa) with specialization in Topology and is presently a research associate at Quantum Leap Africa (QLA, Rwanda). His research interests are computational geometry, applied algebraic topology (topological data analysis), and geometric machine learning (graph and point-cloud representation learning). His current focus lies in geometric methods in data analysis, and his work seeks to develop effective and theoretically justified algorithms for data/shape analysis using geometric and topological ideas and methods.
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