Machine learning is a collection of techniques for understanding data, including methods for visualization, prediction, classification and other tasks relevant to data analysis. However, as data continues to grow in size and dimensionality, making sense of the outputs of machine learning algorithms becomes extremely difficult without having to trim down the size of the data set.

During the webinar Alexis Johnson will discuss how Topological Data Analysis provides a framework for characterizing shape in complex data. This shape can be used to study the underlying structure of the data, identify sub-populations, and statistically explore their distinguishing characteristics.

Listen to the talk here.

Machine learning is a collection of techniques for understanding data, including methods for visualization, prediction, classification and other tasks relevant to data analysis. However, as data continues to grow in size and dimensionality, making sense of the outputs of machine learning algorithms becomes extremely difficult without having to trim down the size of the data set.

During the webinar Alexis Johnson will discuss how Topological Data Analysis provides a framework for characterizing shape in complex data. This shape can be used to study the underlying structure of the data, identify sub-populations, and statistically explore their distinguishing characteristics.

Listen to the talk here.