Students will learn popular AI/Machine Learning (ML) methods and data science skills through hands-on activities using open-source analytical tools and pre-processed datasets. There are three levels of activities, which can be used standalone or as a series, based on students’ backgrounds. Activity Levels 1 and 2 focus on the most common data format—rectangular data. Specifically, Activity Level 1 leverages a GUI-based tool, requiring no programming skills, while Activity Level 2 encourages basic programming in R and R Studio. Activity Level 3 introduces two types of non-rectangular data analysis—deep learning for imaging data (using Google Colab) and Natural Language Processing (NLP) for text data. Activity Level 3 introduces beginner-level activities rather than programming skills.
To instructors: These activities can be flexibly adjusted and can fit into multiple single-session classes, depending on whether the instructor chooses to let students finish practicing in class or take some parts home. Activity Level 1 is suitable for 1-2 two-hour lab sessions or 1-3 one-hour regular classes; Activity Level 2 is suitable for about 3 two-hour lab sessions or 3-6 one-hour regular classes; and Activity Level 3 is suitable for 1 two-hour lab session or 1-2 one-hour regular classes.
Learning Objectives
- Students will be able to analyze rectangular data using popular AI/ML methods on Weka, a free open-source ML tool. (Activity Level 1).
- Students will learn R programming skills and be able to modify sample code to implement advanced modeling for rectangular data. (Activity Level 2).
- Students will be exposed to deep learning for imaging data classification and NLP techniques for text data processing. (Activity Level 3).