Supervised Machine Learning
with R

 

Overview

R is a powerful language for statistical computing. A prolific user community backs R with with an extensive library of packages. If you can think of it, somebody has already written a library for it. R also has a superb IDE, R Studio, facilitating reproducible research.

This course is for people with some R programming experience. It gives an overview of supervised statistical modeling and machine learning in R. We will focus on a small subset of algorithms and emphasize out-of-sample evaluation.


What You Will Learn

This course introduces R capabilities for regression and classification. Many machine learning algorithms exist and it is only possible to cover a small subset in a single class. We will focus on:

  •  Linear and logistic regression
  •  Decision tree and SVM classifiers
  •  Training sets and test sets
  •  K-fold cross-validation
  •  Prediction vs. inference

Course Outline

The workshop will cover the following:

  • Setting up an R Studio Project and file structure.
  • Review of R, R Studio
  • CRAN task view: machine learning
  • Training, testing, and k-fold cross validation
  • Decision trees and random forests
  • Support vector machines
  • General linear models, focusing on logistic regression
  • Linear regression models

After this course you will have used several supervised machine learning methods. You will understand how to use out-of-sample evaluation methods for your models. Where possible, you will learn to perform inference with these models.


Prerequisites

This workshop requires you to:

  • Bring a computer with wifi connection capability and a power cord
  • Install the latest versions of GNU-R  and R Studio.

Instructor: Tommy Jones

tommyjones.jpg

Tommy is a statistician, mathematician, or data scientist; depending on the problem or audience. He holds an MS in mathematics and statistics from Georgetown University and a BA in economics from the College of William and Mary. He is the Director of Data Science at Impact Research, LLC.

Tommy has previously performed economic and statistical modeling and analysis at the Science and Technology Policy Institute, the Federal Reserve Board, and the Institute for the Theory and Practice of International Relations. He has expertise in regression analyses, time series modeling and forecasting, natural language processing, data mining, and other quantitative techniques.


DATE & TIME: 
SATURDAY, April 30th 2016
9AM-5PM 

LOCATION: 
4601 FAIRFAX DRIVE
ARLINGTON, VA 22203

REGULAR PRICE: $300
EARLY BIRD PRICE: $250
(EXPIRES 4/16/2016)


Buy a course bundle and save!

Two Workshop Bundle - Save 25%

Price

Bundle Price: $450
($225 per workshop)

Description

Attend any two workshops and save 25% off the regular price!
Perfect for those looking to skill-up in a couple data science topics.

Purchase

To purchase this bundle, go to our course bundle registration page.


Three Workshop Bundle - Save 33%

Price

Bundle Price: $600
($200 per workshop)

Description

Attend any three workshops and save 33% off the regular price!
Perfect for those who need a little more exposure to data science.

Purchase

To purchase this bundle, go to our course bundle registration page.


Four Workshop Bundle - Save 42%

Price

Bundle Price: $700
($175 per workshop)

Description

Attend any four workshops and save 42% off the regular price!
Perfect for those looking to gain exposure to several topics.

Purchase

To purchase this bundle, go to our course bundle registration page.