Machine Learning with R – Introduction

About

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

Course leader

Brett Lantz from Sony Intractive Entertainment (https://gserm.org/instructors/brett-lantz/)

Target group

The GSERM Global School in Empirical Research Methods is a high-calibre programme dedicated to teaching cutting-edge research methodology. We not only welcome PhD students, post-docs, and graduate students from all kinds of fields, but also professionals working outside of academia.

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

Course aim

Students will leave with an understanding of the foundational machine learning methods as well as the ability to apply machine learning to their own field of work or study using the R programming language

Fee info