This mini-course will provide a comprehensive introduction to machine learning. Part 1 will briefly overview the full machine learning process and cover introductory concepts such as what is machine learning and why is it used. Popular software libraries will be discussed. Attendees will begin working hands-on in Part 2 to train simple machine learning models. Part 3 covers model evaluation and refinement. Artificial neural networks are introduced during Part 4. The mini-course concludes with a hackathon during Part 5 where participants will work on a small, end-to-end machine learning project chosen from one of multiple domains.
Attendees should have some familiarity with Python and basic calculus.
The Introduction to Machine Learning mini-course will be held during Wintersession 2023 on January 17, 18, 19, 23, 24 in Lewis Library 138 at 2:00-4:00 PM.
The materials in this repository were created by Brian Arnold, Gage DeZoort, Jonathan Halverson, Christina Peters, Savannah Thias and Amy Winecoff.