Predictions and learning with random monomial ideals

Many computational problems on polynomial ideals are known to have bad worst-case running times. Can we instead make a quick probabilistic guess at the answer? One strategy is to understand what properties are very likely to occur in a given random model. As the degree of the generators grow, we asymptotically almost surely predict projective dimension and Cohen-Macaulayness of monomial ideals, adding to previous work on Krull dimension. Another approach is to use machine learning to allow a computer to make predictions. We train neural networks to estimate Krull dimension and projective dimension of monomial ideals with good accuracy. This is joint work with Jesus de Loera, Serkan Hosten, Lily Silverstein, and Zekai Zhao.