Abstract: Algebraic geometry and combinatorics have a long-established culture of producing and interrogating classification datasets. These datasets can be at the limit of current computing resources, e.g., the Kreuzer-Skarke classification has almost half-a-billion entries. The time is ripe to explore the landscape of algebra-geometric datasets by means of data science techniques. In this talk, I will report on initial joint work with Bao, He, Hirst, Kasprzyk, and Majumder investigating the effectiveness of machine learning (ML) methods for predicting properties of Hilbert series and lattice polytopes. Our results support the idea that in many cases a mathematical theorem underlies ML predictions of high accuracy. Furthermore, our observations show that ML can also provide hints to new exciting and unexpected relations.