Intermediate R - Introduction to Machine Learning, Goldsmiths, University of London, Monday, 23. September 2019

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.

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

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.

10% if you are taking two courses in consecutive weeks
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

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@, or alternatively, 020 7078 5468.

Monday, 23. September 2019, Goldsmiths, University of London, Intermediate R - Introduction to Machine Learning

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