The Shape of Data Network Science, Geometry-Based Machine Learning, and Topological Data Analysis in R

Using case studies and illustrations, this advanced machine learning book walks readers through the ways geometry and topology influence machine learning, giving them a new toolkit to use in their AI work, and introducing them to exciting, cutting-edge algorithms.

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.

Colleen M. Farrelly is a senior data scientist whose academic and industry research has focused on topological data analysis, quantum machine learning, geometry-based machine learning, network science, hierarchical modeling, and natural language processing. Since graduating from University of Miami with an MS in Biostatistics, Colleen has worked as a data scientist in a variety of industries, including health care, consumer packaged goods, biotech, nuclear engineering, marketing, and education. Colleen often speaks at tech conferences, including PyData, SAS Global, WiDS, Data Science Africa, and DataScience SALON. When not working, Colleen can be found writing haibun/haiga or doing any sort of water sport.

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.