This course is a hands-on course covering the use of statistical machine learning methods available in R.

The following basic learning methods will be covered and used on common datasets.

• classification trees (rpart)

• feed-forward neural networks and multinomial regression

• random forests

• boosting for classification and regression

• bagging for classification and regression

• penalized regression modelling (lasso/ridge regularized generalized linear models)

• model based recursive partitioning (trees with statistical models at the nodes)

• training and evaluation will be done through the use of the caret and ROCR packages

Course duration: 2 days.

Instructor: Jan Wijfels, BNOSAC

 Jan Wijffels is the founder of www.bnosac.be – a consultancy company specialised in statistical analysis and data mining. He holds a Master in Commercial Engineering, a MSc in Statistics and a Master in Artificial Intelligence and has been using R for 8 years, developing and deploying R-based solutions for clients in the private sector. He has developed and co-developed the R packages ETLUtils and ffbase.

Please register and pay through Eventbrite