Tutorials

 

Introduction to Artificial Intelligence

Instructor: Dr. Patrick Martin This lesson defines artificial intelligence, summarizes its history, and suggests concepts we need to keep in mind as AI is integrated into our daily lives.

 

Introduction to Machine Learning

Instructor: Dr. Patrick Martin This lesson discusses how software uses statistics to find patterns in data and how machine learning differs from other decision-making systems.

 

Sample lesson: George Mason University SYST530: Data-Driven Risk Estimation, taught by Dr Phil Barry

This sample lesson is for a George Mason University class called SYST530: Data-Driven Risk Estimation, taught by Dr. Phil Barry. The materials were created by Joe Garner, an instructional designer at MITRE, and Ali Zaidi, a MITRE data scientist. It includes the lesson plan, an instructional design guide, and the lesson notebook used in his class.

 

Generation AI Instructional Design Guide for Your Lessons

Your data science and machine learning lessons are likely to be more successful if you develop them using instructional design principles such as establishing outcomes, aligning lessons to learning objectives, asking questions that lead to enduring understanding, prioritizing the learning objectives, and mapping assessments to outcomes.

 

Responsible Data Practices in Machine Learning

In this notebook, Jonathan Rotner presents a data analysis pipeline in Python to demonstrate what a typical data science workflow looks like. In addition to providing code examples, the notebook conveys responsible data practices so that you can be a more effective and collaborative data scientist.