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Tomorrow’s Machine Learning Today: Topological Data Analysis, Embedding, and Reinforcement Learning

Dr. Jans Aasman was interviewed for this Inside Big Data Article:

“On the one hand you have symbolic rules based systems like graph databases that are really good at reasoning,” reflected Franz CEO Jans Aasman. “Then you have statistical machine learning, which is very good at perceiving and learning. You want to use them together because together, it gets really, really good.”

Topological data analysis is arguably at the vanguard of machine learning trends because of its fine-grained pattern analysis that supersedes that of traditional supervised or unsupervised learning. Although technically part of unsupervised learning, topological data analysis “is a clustering technique where you get way better results,” Aasman explained. Clustering is a visual analytics approach supported by graphs that reveal where data are populated according to certain segments. Aasman used a simple example to explain the effectiveness of topological data analysis: “There’s five positions in basketball, but then if you analyze the players based on a set of features, you find that there’s like, 200 types of basketball players.”

Read the full article at Inside BigData.