Statistical Machine Learning with R
18 Oct 2022 - 19 Oct 2022
This course is a hands-on course covering the use of statistical machine learning methods available in R.
Practical information:
18 Oct 2022 - 19 Oct 2022
Vandenheuvelinstituut, Dekenstraat 2, 3000 Leuven
English
Target audience: R users in industry/academics who are interested in building predictive models in R.
Want to register?
- Prerequisites: Initial experience in R ranging from a few weeks to several years. Some practical experience in regression modelling.
- Price: €100-€600
Georganiseerd door:
Course outline
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
The course will cover the techniques from a high-level viewpoint, useful for day-to-day R users.
Target audience
- The course is for R users in industry/academics who are interested in building predictive models in R which have some experience with regressions but have less knowledge of machine learning and techniques of artificial intelligence.
- Also persons interested in the statistical learning techniques itself will find this course usefull.
- Or people with a data science background with less knowledge of R and which are interested in machine learning in general.
Profound knowledge of statistics is required as background.
Teacher / speaker
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 10 years, developing and deploying R-based solutions for clients in the private sector. He has developed and co-developed the R packages ffbase, ETLUtils, RMOA and RMyrrix.
