Introduction to Machine Learning with R

The course will cover a number of methods for both prediction and classification problems using both supervised and unsupervised machine-learning techniques in R.

The course is available in four formats:

• Campus
• Clients office
• Online (virtual classroom) with live tutoring
• Bespoke (group or 1:1 coaching)

100% of AIR income, beyond operational expense, is used towards education and research. Read more…

 

Explore machine learning

Machine learning is a branch of computer science that studies the design of algorithms that can learn. This course will allow you to get to grips with machine learning through the use of R in order to address problems and discover methods for the prediction and classification problems.

Designed for people who have either completed our Introduction to R course or who have experience using it, you’ll use supervised and unsupervised machine-learning techniques and apply them to a variety of real-world datasets, such as house price data or demographic data.

Throughout the course, you’ll learn the following techniques:

Supervised learning:

• Classification: Decision trees, logistic regression
• Numeric prediction: Regression trees, linear regression

Unsupervised learning:

• Pattern discovery: Association rules
• Clustering: K-means clustering

By the end of the course, you’ll have built a library of re-usable code and understand how to solve more common problems.

For this course, you’ll need some basic experience with R, be able to conduct simple statistical analysis. You also have the option to bring your own dataset if you have it. 

On this course, we want you to have as much hands-on experience as possible. We’ll focus on teaching you to apply machine-learning techniques to data, rather than focusing on the more theoretical points. However, we will point you toward online content that will help you with the more theoretical side and prepare you for any future courses you decide to go on.

Campus Courses - VAT-free: Our venues are in central London (Senate House) and in New Cross. You can select your preferred location by choosing the date available.

Virtual Classroom Courses - VAT-free: Live tutoring brings all the benefits of face to face training and, in addition, improves skills for ‘digital’ participation. There is an increasing need for online activities in all areas of life – making ‘digital’ participation an essential skill of the 21st century. 

Bespoke Courses:  If you are interested in a tailor-made Public Speaking course option: 1-1 coaching or bespoke group training, please contact us using the email or phone number provided below.

Fees

£495  

Booking information

As a University, we want to give as many people access to our courses as possible. There are a number of discounts that can help you, find out more below: 

• 10% discount when you enrol for more than one of our short courses at the same time.
• 20% discount for UK students
• 25% discount for members of the UK Law and Society Association (UKLSA)
• If 5 people register from the same institution, the fifth place is free.
• If you’re a Goldsmiths’ student, member of staff and alumni, you can 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.

To find out about our refund policy, go to our AIR courses main page

Disability Support

We are committed to providing reasonable teaching adjustments for students with disabilities that may impact on their learning experience. If you require adjustments, please complete the relevant section on the booking form and also contact us at shortcourses@gold.ac.uk so we can respond to your requests as soon as possible. 

Please note our short courses sell-out quickly, so early booking is advisable.

Book now

Enquiries

If you have any questions about this course please contact air (@gold.ac.uk) or call +44 (0)20 7078 5468.

For information on our upcoming short courses please sign up to our mailing list.

Tutor information