Introduction to Machine Learning

This 10-week course will introduce students to foundational concepts in Machine Learning (ML) practice, building on their prior exposure to probability and statistical concepts. The class will cover the theoretical underpinnings of machine learning practices but focus on practical uses. Students with less quantitative experience will gain exposure to the mathematics behind the basic methods. Students with a significant quantitative background will explore the full life cycle of a data science project and best practices.

Upon completion of this course, a student should understand:

• The workflow for an ML project
• The basic mechanics of key ML techniques
• How these methods fail, and
• How to be a critical consumer of ML analysis.