Richard Wong, Ph.D.

Course: Introduction to AI/ML

Description:  This accelerated six-week free course will introduce you to ML theory and applied ML, focusing on the skills and tools needed to become a practicing data scientist. The course is divided into six sessions, each two hours in duration. The first three sessions will focus on the fundamentals of machine learning. The remaining three will focus on applying machine learning theory through the lens of some of the most popular models. Each application session will involve using Python programming examples and a mini project. At the end of the six-week session, a capstone project will be assigned, which is intended to bring together all elements covered throughout the course. Completing projects will be optional but highly recommended to maximize the course's benefit and cement the skills you learned. After completing this course, each participant will understand what machine learning is, how it differs from econometrics, how to implement ML algorithms in Python language, and how to apply machine learning to answer real-world problems. While prior knowledge of linear algebra, vector calculus, optimization, and probability theory would facilitate learning ML, I will assume no prerequisite knowledge of these fundamentals. Instead, I will cover all required knowledge in each session.

Syllabus: Introduction to Machine Learning

Date: Summer 2023