Advances in machine learning (ML) over the past half-dozen years have revolutionized the effectiveness of ML for a variety of applications. However, design processes present challenges that require synergetic advances in ML and CAD as compared to traditional ML applications. As such, the purpose of the symposium is to discuss, define and provide a roadmap for the special needs for ML for CAD where CAD is broadly defined to include both design-time techniques as well as run-time techniques. Topics of interest to this symposium include but are not limited to: • LLM-CAD: Large Language Model for CAD • ML approaches to logic design. • ML for physical design. • ML for analog design. • ML for FPGA designs. • ML methods to predict and optimize circuit aging and reliability. • Labeled and unlabeled data in ML for CAD. • ML for power and thermal management. • ML techniques for resource management in many-cores. • ML for Design Technology Co-Optimization (DTCO). • ML for design verification.

ACM/IEEE 7th Symposium on Machine Learning for CAD (MLCAD) is technically sponsored by IEEE. The conference proceedings are likely to be indexed in databases such as Scopus, Web of Science (WoS), Google Scholar, and others.