Machine Learning for Everyone: Introduction to Classification and Clustering

Summary	This Novel AI assignment module introduces two important machine learning approaches: Classification and Clustering. Each approach provides a way to group things together, the key difference being whether or not the groupings to be made are decided ahead of time. While these grouping techniques are a type of Artificial Intelligence designed to be performed by a computer, they can be used on any sort of data from any discipline. The module provides background for an instructor in any discipline to incorporate the material into a lesson for students in any discipline where machine learning may be applicable. It is aimed at a beginner, non-technical student audience, though it is also appropriate as an introduction for students with some computing experience. Materials for hands-on exercises and pre- and post-test quizzes are included.
Topics	Machine Learning, Artificial Intelligence, Supervised Learning, Unsupervised Learning, Classification, Clustering
Audience	Undergraduate college students in any discipline where exposure to introductory machine learning concepts may be beneficial. Also appropriate for college majors in a computing discipline as a first introduction to applications of machine learning. May also be suitable for high school students.
Difficulty	Beginner level, very accessible material requiring up to one typical class meeting or lecture period.
Strengths	Use of unplugged activities, pencil and paper activities, intuitive introduction to machine learning understandable by non-technical students, introduces applicablility to many other disciplines, no computers required, usable by instructors in any discipline as it requires no in-depth background in the topics covered.
Weaknesses	Not scalable to class sizes over 30, does not discuss mathematical underpinnings of concepts presented.
Dependencies	No specific pre-requisite knowledge needed, although coverage of our related "Introduction to Machine Learning" module is beneficial though not necessary.
Variants	Instructors in many other disciplines, including non-computing disciplines, can follow ideas in material to apply Classification and Clustering techniques into those disciplines and generate student interest in applications of machine learning to their own discipline. Instructors can customize activities and create relevant assignments that build on concepts presented using data and examples from their own disciplines as described in provided material.

Assignment Materials

Introduction to Classification and Clustering.pdf - An overview and introduction to the module containing most or all of the background information you will need to make use of this module. Start with this document, and use it as a guide. It contains a detailed, step-by-step lesson plan and the answer key for the Test Your Knowledge quiz that can be given to students at the conclusion of the lesson.
Introduction to Classification and Clustering.pptx - PowerPoint slides for use in teaching the topic. Among the slides are examples of Classification and Clustering designed to be done by students during the class, using the handout.
Hands on Activity - Classification and Clustering.pdf - A handout containing hands-on classification and clustering activities.
Start Up Survey and Test Knowledge Quiz.pdf - An easy-to-complete survey for students to take before beginning this module and a follow-up quiz that measures student learning and provides a before and after comparison with the initial survey. The "Start-up Survey" should be given to students before any of the module material is covered to capture the "before" level of understanding. The "Test Your Knowledge Quiz" should be given at the conclusion of the module as a way to see the "after" level of understanding, to demonstrate student learning. The survey and quiz are optional, but can serve as a valuable means to verify student learning. If you use these, and your institutional rules permit it, please share the results with us. Thanks!
Clustering Birds Activity.pdf - Optional additional activity to provide more experience with clustering.
Discussion of Learning Measures.pdf - Presentation of results of using a basic learning measure and discussion of various observations that may inform future offerings of the material.
Modules Overview.pdf - Description of project and listing of available modules related to this module.