2018 Machine Learning Home

 

beyond p bear

Image: Courtesy of Philippe Naveau

Course Description

This three-day course, April 17-19, 2018, will provide an introduction to fundamental methods used in Machine Learning.  We will start with dimension reduction methods, which are often used as precursors to subsequent analysis.  This will be followed by an overview of unsupervised vs. supervised learning.  For unsupervised learning, we will cover various cluster analysis methods such as k-means.  For supervised learning, we will introduce data-driven approaches such as regression trees and modeling-based approaches with a special focus on artificial neural networks and deep learning.  The course is aimed at an applied audience and will make heavy use of data examples to illustrate the concepts.  We'll use the open-source statistical software R [https://www.r-project.org/]. The format of the course is hands-on and participants will use their own laptops.

Instructors

The lead instructor for the course is Valerie Monbet, Professor of Statistics at the University of Rennes. She will be assisted by graduate students and post-doctoral fellows specializing in Statistics and Machine Learning. Seats are limited to 12 participants to allow for effective one-on-one coaching. To apply, please visit the Machine Learning Application link on the left hand-side of the page.  The application deadline is March 2, 2018, at 5:00 PM (MST).

This training is for UCAR employees only.