In this talk, I will discuss interesting ML techniques for challenging EDA optimization problems. I will cover commonly used ML techniques such as sequential model-based optimization and reinforcement learning for optimization. I will also introduce two promising techniques: self-supervised learning (SSL) and gradient descent based optimization leveraging deep learning frameworks and architectures. SSL learns the optimized EDA solution data manifold. Conditioned on the problem input, it can directly produce the optimized solution. Gradient descent based optimization approach is very efficient for optimization in high dimensional spaces. Powered by deep learning frameworks and architectures, it can solve many EDA optimization problems efficiently. I will illustrate the applications of these techniques in various physical design problems and discuss the challenges of applying these techniques. Finally, I will outline three main approaches to integrate ML and conventional EDA algorithms together and explain the importance of integrating ML as well as GPU acceleration for EDA.