Numerical Optimization for Data Science and Machine Learning

1 Course Unit

Optimization is the modeling language in which modern data science, machine learning, and sequential decision-making problems are formulated and solved numerically. This course will teach students how to formulate these problems mathematically, choose appropriate algorithms to solve them, and implement and tune the algorithms in the software PyTorch. PyTorch is a freely available machine learning library is recognized as one of the two most popular machine learning libraries alongside TensorFlow. By the end of the course, students will become an intelligent consumer of numerical methods and software for solving modern optimization problems. Prerequisite: (MATH 2400 OR MATH 2200) AND (STAT 4300 OR ESE 3010) 1 Course Unit