Students will learn popular AI/Machine Learning (ML) methods and data science skills through hands-on projects using open-source analytical tools and pre-processed real-world public datasets. There are three single-session classroom activities, which can be used standalone or as a series. These activities are designed in three levels. Activity levels 1 and 2 focus on the most common data format—rectangular data—while level 1 leverages a GUI-based tool, Weka, so no programming is needed, and level 2 encourages basic programming on R and R Studio (or Python per request). Level 3 introduces two types of non-rectangular data analysis—deep learning for imaging data (using Weka, GPU needed) and Natural Language Processing (NLP) for text data (using CLAMP). No programming is needed for level 3.
Learning Objectives
- Students will be able to analyze rectangular data using popular AI/ML methods on Weka. (Activity Level 1).
- Students will learn R (or Python per request) programming and be able to modify sample code for advanced modeling for rectangular data. (Activity Level 2).
- Students will be exposed to deep learning for imaging data processing using Weka. (Activity Level 3).
- Students will be exposed to NLP techniques for text data processing using CLAMP. (Activity Level 3).