Poster presented at the Geometric Sciences of Information 2019 (GSI2019) conference | Aug 27-29, 2019 |Toulouse (France). Information cohomology is a branch of topological data analysis that allows us to quatify directly statistical dependencies and independences in a given dataset. Theorems establish Shannon entropy as a first cohomology class and mutual information as coboundaries on finite probability space endowed with a random variable chain complex structure. We present some simplicial subcase applications to supervised and unsupervised learning in different contexts: transcriptomic and digits and medical CT image classification.