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