£495

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Introduction to Machine Learning with R

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Goldsmiths, University of London

8 Lewisham Way

London

SE14 6NW

United Kingdom

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Refunds up to 7 days before event

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· Mon 13 - Wed 15 Apr 2020, 10:00 - 17:00 (New Cross) - to book select ticket option for 13 Apr 2020

· Mon 17 Aug - Wed 19 Aug 2020, 10:00 - 17:00 (New Cross) - to book select ticket option for 17 Aug 2020


This is an intermediate course for those who have either completed an ‘Introduction to R’ type course or who have equivalent experience using R. We will cover a number of methods for both prediction and classification problems using both supervised and unsupervised machine-learning techniques in R.


R makes the implementation of advanced machine learning techniques a relatively straight forward process – we will harness these techniques to address problems such as house price prediction, customer segmentation and election result prediction.

Supervised learning
• Classification (e.g. predicting whether a passenger will survive or die on the titanic based on demographic information): Decision trees, logistic regression
• Numeric prediction (e.g. what is the correct value of a house): Regression trees, linear regression
Unsupervised learning
• Pattern discovery (e.g. which items are commonly bought together): Association rules
• Clustering (finding distinct groups in data e.g. groups of individuals with similar shopping behaviour): K-means clustering

We will apply these techniques to a variety or real world datasets, such as house price data, financial data and demographic data. You will also be encouraged to source your own datasets to test your skills.

This course will take a practical approach; we will prioritise getting used to applying machine-learning techniques to data and interpreting results, rather than focusing on theoretical points. However, we will point you towards online content to help you with the theoretical side and prepare you for future learning in the field.

By the end of the course you will have built a library of re-usable code and an understanding of how to solve common problems and where to look for useful guidance. This will help you in your future learning and implementation of machine learning techniques.

Prerequisites:

  • Some basic experience with R
  • Conducting simple statistical analyses (e.g. obtaining means, medians and standard deviations)
  • BYO (Bring Your Own) Dataset - optional

Tutoring

The course is directed by Dr Will Lawrence, who completed his PhD at the department of Electronics and Computer Science at the University of Southampton, and who has a background in psychology. Will has rich experience in delivering training in both Python and R, to diverse audiences.

Discounts

  • 10% when a participant enrolls for more than one of our courses (at the same time)
  • 20% UK students
  • 25% UK Law & Society Association (UKLSA) Members
  • If five people register from the same institution for the same intake, the fifth place is free
  • Goldsmiths students, staff and alumni - email us for current discounts


As a University, we are able to offer our courses at minimum prices, and free of VAT - to make knowledge available to as wide audience as possible.

FAQs

How can I contact the organiser with any questions?

For any further enquiries or to receive the code to qualify for a discount, please contact at us at air@gold.ac.uk, or alternatively, 020 7078 5468.

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Location

Goldsmiths, University of London

8 Lewisham Way

London

SE14 6NW

United Kingdom

View Map

Refund Policy

Refunds up to 7 days before event

Eventbrite's fee is nonrefundable.

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