# Content

- Getting started with R
- Regression Models
- linear regression
- multiple linear regression
- performance of regression models

- Classification Models
- logistic regression
- performance of binary classification models

- Learning Machines (AIML)
- supervised learning
- Decision Tree
- Random Forest
- Support Vector Machine

- unsupervised learning

# Course Materials

- the code that we use on this workshop: R-code overview
- Basics of R and statistics:
- code for quick overview: R-code from the slides
- slides from the book, part II: slides
- R-code from these slides: R-code
from the slides
- excercises

- Data import:
- slides based on part II of the
book
- R-code from those slides

- Data wrangling:
- Building Powerful Models:
- Credit Model (from the sample materials for the book): R
Markdown examples – locate the credit model example