Mathematical reasoning is a fundamental aspect of human cognition that has been studied by scholars ranging from philosophers to cognitive scientists and neuroscientists. Mathematical reasoning involves analyzing complex information, identifying patterns and relationships, and drawing logical conclusions from evidence. It is central to many applications in science, engineering, finance, and everyday contexts. Recent advancements in large language models (LLMs) have unlocked new opportunities at the intersection of artificial intelligence and mathematical reasoning, ranging from new methods that solve complex problems or prove theorems, to new forms of human-machine collaboration in mathematics and beyond. Our proposed workshop is centered on the intersection of deep learning and mathematical reasoning, with an emphasis on, but not limited to, large language models. Our guiding theme is: "To what extent can machine learning models comprehend mathematics, and what applications could arise from this capability?'' To address this question, we aim to bring together a diverse group of scholars from different backgrounds, institutions, and disciplines in our workshop. By hosting this workshop, we hope to stimulate insightful discussions that will guide future research and applications in this rapidly expanding field.