Machine learning (ML) and pattern matching (PM) are powerful computer science techniques which can derive knowledge from big data, and provide prediction and matching. Since nanometer VLSI design and manufacturing have extremely high complexity and gigantic data, there has been a surge recently in applying and adapting machine learning and pattern matching techniques in VLSI physical design (including physical verification), e.g., lithography hotspot detection and data/pattern-driven physical design, as ML and PM can raise the level of abstraction from detailed physics-based simulations and provide reasonably good quality-of-result. In this paper, we will discuss key techniques and recent results of machine learning and pattern matching, with their applications in physical design.