Machine Learning with R – Introduction

Prerequisites (knowledge of topic)

This course assumes no prior experience with machine learning or R, though it may be helpful to be familiar with introductory statistics and programming.



A laptop computer is required to complete the in-class exercises.



R ( and R Studio ( are available at no cost and are needed for this course.


Course content

Machine learning, put simply, involves teaching computers to learn from experience, typically for the purpose of identifying or responding to patterns or making predictions about what may happen in the future. This course is intended to be an introduction to machine learning methods through the exploration of real-world examples. We will cover the basic math and statistical theory needed to understand and apply many of the most common machine learning techniques, but no advanced math or programming skills are required. The target audience may include social scientists or practitioners who are interested in understanding more about these methods and their applications. Students with extensive programming or statistics experience may be better served by a more theoretical course on these methods.



The course will be designed to be interactive, with ample time for hands-on practice with the Machine Learning methods. Each day will include several lectures based on a Machine Learning topic, in addition to hands-on “lab” sections to apply the learnings to new datasets (or your own data, if desired).


The schedule will be as follows:


Day 1: Introducing Machine Learning with R

How machines learn

Using R, R Studio, and R Markdown

k-Nearest Neighbors

Lab sections – installing R, using R Markdown, choosing own dataset (if desired)


Day 2: Intermediate ML Methods – Classification Models

Quiz on Day 1 material

Naïve Bayes

Decision Trees and Rule Learners

Lab sections – practicing with Naïve Bayes and decision trees


Day 3: Intermediate ML Methods – Numeric Prediction

Quiz on Day 2 material

Linear Regression

Regression trees

Logistic regression

Lab sections – practicing with regression methods


Day 4: Advanced Classification Models

Quiz on Day 3 material

Neural Networks

Support Vector Machines

Random Forests

Lab section – practice with neural networks, SVMs, and random forests


Day 5: Other ML Methods

Quiz on Day 4 material

Association Rules

Hierarchical clustering

k-Means clustering

Lab section – practice with these methods, work on final report






Machine Learning with R (2nd ed.) by Brett Lantz (2015). Packt Publishing


Supplementary / voluntary

None required.


Mandatory readings before course start

Please install R and R Studio on your laptop prior to the 1st class. Be sure that these are working correctly and that external packages can be installed. Instructions for doing this are in the first chapter of Machine Learning with R.


Examination part

60% of the course grade will be based on a project and final report (approximately 2-3 pages), to be delivered within 2-3 weeks after the course in R Notebook format. This will be graded based on its use of the methods covered in class as well as making appropriate conclusions from the data. The remaining 40% will be based on four short quizzes, which are based on the topics covered on the previous day.


Supplementary aids

Students may reference literature and class materials as needed when writing the final project report. The short in-class quizzes will be closed book and will measure the student’s understanding of the material covered in the previous day’s class.


Examination content

The final project report should illustrate an ability to apply machine learning methods to a new dataset, which may be on a topic of the student’s choosing. The student should explore the data and explain the methods applied.



The quiz material will be based entirely on the lecture material, which is based on the required book, Machine Learning with R (2nd edition). Note that the lectures may include a small amount of additional material not found in the book.