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Course Details

Course Outline
Learning Outcomes
Materials
Prior Knowledge

Course Outline

Introduction to analytics: a general introduction into analytics and some of the techniques that are in common use.
Simple regression problems: simple and multiple linear regression and model diagnostics.
Model selection and assessment: Cross validation and bootstrapping. Penalised regression and shrinkage.
Classification: KNN, clustering, logistic regression, Linear Discriminant analysis and associated diagnostics.
Advanced regression techniques: polynomial regression, splines, local regression, GAMs, trees and random forests.
Workflow development: Throughout we will develop a workflow of training, testing and assessing models that can be extended to techniques not directly covered.

Learning Outcomes

By the end of the course participants will be able to…

have a thorough understanding of popular analytical techniques practised in industry today
assess model validity through bootstrapping and cross validation
understand which technique applies to their own data
efficiently and effectively analyse their own data using said techniques in R
apply the developed workflow practice to other modelling techniques

Materials

Prior Knowledge

It will be assumed that participants are familiar with R. For example inputting data, basic visualisation, basic data structures and use of functions. Attending the Introduction to R course will provide a sufficient background, but the programming with R will be helpful.

Training Course Details

Machine Learning with R

Course Level: Intermediate