Data Analysis ENSIIE
Projects
 Projet 2022 Kmeans++
 Projet 2023 Generalized Kmeans
 EM Algorithm (2023)
 Implement the initialization of the EM algorithm
 Implement the Estep
 Implement the Mstep
 Test your EM algorithm on the simulated data from Exercise 1 of the worksheet
Lectures Notes
Exercices
 Exercices on multivariate normal
 Exercices on multivariate normal correction
 Exercices on clustering
 Exercices on mixture models
 Exercices on PCA
 Exercices on Kernels
Elements of solution
Data files
Document and Links
Specific reference
Reference books about machine learning

Machine Learning: A Probabilistic Perspective from Kevin P. Murphy
 Chapter 4: Gaussians models
 Chapter 11: Mixture Models and EM algo (with kmeans)
 Chapter 25: Clustering (HAC)
 Chapter 12: Latent Linear Models (PCA)
 Chapter 14: Kernels

Pattern Recognition and Machine Learning from Chris M Bishop
R base
Official manuals about R base can be retrieved from
https://cran.rproject.org/manuals.html
Contribution by the community can be retrieved from
https://cran.rproject.org/otherdocs.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/