This course introduces the cutting-edge research on combining reasoning with machine learning. We will first study the logical foundations and algorithms behind many reasoning engines, and then learn how machine learning can be used to improve complicated reasoning systems. This course will cover the following topics: Boolean Satisfiability (SAT), Satisfiability Modulo Theories (SMT), program synthesis, statistical approaches for software debugging, inductive logic programming, and neuro-symbolic systems. Extensive paper readings, in-class discussions, and project presentations are expected.