Introduction to machine learning
Projects
- to come
Lectures Notes
Exemples
Exercices
Document and Links
Reference books about machine learning
-
Probabilistic Machine Learning An Introduction from Kevin P. Murphy
- Chapter 21: Clustering
- Chapter 20: Dimension Reduction
- Chapter 5: Decision Theory
- Chapter 9: Linear Discriminant Analysis
- Chapter 18: Trees, Forests, Bagging, and Boosting
-
Pattern Recognition and Machine Learning from Chris M Bishop
R base
Official manuals about R base can be retrieved from
https://cran.r-project.org/manuals.html
Contribution by the community can be retrieved from
https://cran.r-project.org/other-docs.html
The short introduction from Emmanuel Paradis allows a quick start
- ‘‘R for Beginners’’ by Emmanuel Paradis.
Longer book allow a deepening. See for example
- ‘‘Using R for Data Analysis and Graphics - Introduction, Examples and Commentary’’ by John Maindonald.
R from RStudio developers
- R for Data Science The book of Wickam about more recent R development for data science
And if you want more see https://www.rstudio.com/resources/books/