Modern compilers try to optimize programs with respect to a given objective, for example, program performance or memory consumption. The optimizations typically rely on information that is only available at run-time and therefore has to be over-approximated by the compiler. This may severely limit the optimization opportunities and, thus, the run-time performance. In this dissertation, we present our framework for intelligent speculative compiler optimizations. The framework uses machine learning to provide compilers with knowledge about the run-time behavior of programs to bridge the gap...