Winter School in Empirical Research Methods
MACHINE LEARNING WITH R – INTRODUCTION
Machine learning, put simply, involves teaching computers to learn from experience, typically for the purpose of identifying and/or responding to patterns as well as making predictions about what may happen in the future. This course is intended to be an introduction to machine learning through the exploration of real-world examples. We will cover the basic math and statistical theory needed to begin applying 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 applying them to their own work. Students with extensive programming or statistics experience may be better served by a more advanced course on the topic.
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.
The course will be designed to be extensively interactive, with ample time for hands-on practice and group problem solving. Each day will include several 45-minute lecture periods, each based around a Machine Learning topic, in addition to a longer hands-on “lab” section to conclude the day. Throughout the course, students will be encouraged to identify a project and dataset to work on individually or in groups during the lab sessions. Students will be asked to write a report on this project after completion of the course.
For more information click "LINK TO ORIGINAL" below.