This is an advanced project course in data science that offers a "maker" and/or "innovation" viewpoint. The course is focused first on developing an open-ended-real world project relating to data science. Related concepts of computer science tools and theoretical concepts are covered to support the project. These concepts include filtering, prediction, classification, LTI systems, and spectral analysis. After reviewing each concept, we explore implementing it in Python using libraries for math array functions, manipulation of tables, data architectures, natural language, and ML frameworks.

This is the second graduate course in the machine learning series, following IEOR 242. This course continues the discussion of key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Students will gain experiences working with various analytics packages in Python, and deliver a comprehensive team project. Topics include: deep learning, reinforcement learning, end-to-end learning, casual interference, and ethics, fairness and safety in artificial intelligence. students will gain experience understanding and applying these techniques.

Repeat Rules

Course is not repeatable for credit.