Machine learning is a powerful computer science technique that can derive knowledge from big data and make predictions/decisions. Since nanometer integrated circuits (IC) and manufacturing have extremely high complexity and gigantic data, there is great opportunity to apply and adapt various machine learning/deep learning techniques in IC design and technology co-optimization (DTCO), including physical verification and physical design. This talk will first give an overview on machine learning/deep learning, and then discuss several case studies in various DTCO applications, including lithography modeling and hotspot detection, optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion, and physical design. It will further discuss some challenges and research directions.