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Universidad EAFIT
Carrera 49 # 7 sur -50 Medellín Antioquia Colombia
Carrera 12 # 96-23, oficina 304 Bogotá Cundinamarca Colombia
(57)(4) 2619500 contacto@eafit.edu.co
EAFITEscuela de VeranoEscuela de Verano / CursosMachine learning with R- introduction - 1st Session

Machine learning with R- introduction - 1st Session

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.

Dates:  from december 3rd  to december 7th

Inversión:  1000 francos Suizos 

Term: 18 hours


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.


Hardware

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


Software

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


Instructor

Brett Lantz.jpeg

Brett Lantz

Brett Lantz is a data scientist at the University of Michigan and the author of Machine Learning with R, a best-selling textbook praised for its beginner-friendly practical approach to the topic. After studying sociology and machine learning at the University of Michigan (B.S.) and University of Notre Dame (M.A.), Brett has spent more than 10 years using innovative data methods to understand human behavior. First captivated by machine learning while studying a large database of teenagers’ social network profiles, Brett has since worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others.

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