Abstract Mixed Integer Programs (MIP) are solved exactly by tree-based branch-and-bound search. However, various components of the algorithm involve making decisions that are currently addressed heuristically. Instead, I propose to use machine learning (ML) approaches such as supervised ranking and multi-armed bandits to make better-informed, input-specific decisions during MIP branch-andbound. My thesis aims at improving the overall performance of MIP solvers. To illustrate the potential for ML in MIP, I have so far tackled branching variable selection, a crucial component of the search procedure, showing that ML approaches for variable selection can outperform traditional heuristics.