This course will investigate data mining and machine learning algorithms in both supervised and unsupervised learning. Students will understand how to use the R programming language for performing clustering, classification, and regression analysis.  Students will learn the capabilities and operation of many algorithms including decision trees, k-means, k-nearest neighbors, linear regression, ID3 for Decision Trees, and the Perceptron.

Learning Objectives and Outcomes:

By the end of this course students will be able to:

1. Explain the differences among the three main styles of learning: supervised, reinforcement, and unsupervised.
2. Implement simple supervised learning, reinforcement learning, and unsupervised learning examples using R.
3. Understand a range of machine learning algorithms along with their strengths and weaknesses.
4. Understand the basic operation of machine learning algorithms including decision trees, neural networks, K nearest neighbors, K means clustering, and regression.
5. Be able to apply machine learning algorithms to solve simple problems.

Course Schedule and Topics

This course will cover the following topics in eight learning sessions, with one Unit per week. The Final Exam will take place during Week/Unit 9 (UoPeople time).

Week 1: Unit 1 – Introduction to Data Mining and Machine Learning

Week 2: Unit 2 – Tools and Technologies for Data Mining and Machine Learning

Week 3: Unit 3 – Regression

Week 4: Unit 4 – Classification

Week 5: Unit 5 – Decision Trees

Week 6: Unit 6 – Artificial Neural Networks – Part 1

Week 7: Unit 7 –  Artificial Neural Networks – Part 2

Week 8: Unit 8 – Unsupervised Learning – Clustering

Week 9: Unit 9 – Course Review and Final Exam

Learning Guide

The following is an outline of how this course will be conducted, with suggested best practices for students.

Unit 1: Introduction to Data Mining and Machine Learning

• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Programming Assignment
• Make entries to the Learning Journal
• Take the Self-Quiz

Unit 2: Tools and Technologies for Data Mining and Machine Learning

• Peer assess Unit 1 Programming Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Programming Assignment
• Make entries to the Learning Journal
• Take the Self-Quiz

Unit 3: Regression

• Peer assess Unit 2 Programming Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Programming Assignment
• Make entries to the Learning Journal
• Take the Self-Quiz
• Complete the first Graded Quiz

Unit 4: Classification

• Peer assess Unit 3 Programming Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Programming Assignment
• Make entries to the Learning Journal
• Take the Self-Quiz

Unit 5: Decision Trees

• Peer assess Unit 4 Programming Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Programming Assignment
• Make entries to the Learning Journal
• Take the Self-Quiz

Unit 6: Artificial Neural Networks – Part 1

• Peer assess Unit 5 Programming Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Begin work on the Simulation Assignment
• Make entries to the Learning Journal
• Take the Self-Quiz
• Complete the Second Graded Quiz

Unit 7: Artificial Neural Networks – Part 2

• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Simulation Assignment started during Unit 6
• Make entries to the Learning Journal
• Take the Self-Quiz

Unit 8: Unsupervised Learning – Clustering

• Peer assess Unit 7 Simulation Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Make entries to the Learning Journal
• Take the Self-Quiz
• Read the Unit 9 Learning Guide carefully for instructions on the Final Exam
• Take the Review Quiz
• Complete and submit the anonymous Course Evaluation

Unit 9: Course Review and Final Exam

• Read the Learning Guide and take the Review Quiz, if you haven’t already done so
• Prepare for, take, and submit the Final Exam
• The Final Exam will take place during the Thursday and Sunday of Week/Unit 9 (UoPeople time); exact dates, times, and other details will be provided accordingly by your instructon