Most real-world AI applications rely on machine learning applied to structured, tabular data. This course is designed to give students practical, hands-on experience with state-of-the-art machine learning tools commonly used in industry to solve real-world problems in science and engineering.

Students will learn to implement complete machine learning pipelines using Scikit-Learn and PyTorch for supervised learning tasks. The course also covers the identification and troubleshooting of common failure modes encountered during model development. Additionally, students will gain experience in developing, training, deploying, and maintaining end-to-end machine learning applications.

Note: This course is not available for credit to Computer Science students.