Data Analysis ENSIIE

Projects

• Projet 2022 Kmeans++
• Projet 2023 Generalized Kmeans
• EM Algorithm (2023)
  • Implement the initialization of the EM algorithm
  • Implement the E-step
  • Implement the M-step
  • Test your EM algorithm on the simulated data from Exercise 1 of the worksheet

Lectures Notes

• Lecture 1 & 2
• Lecture 3
• Lecture 4
• Lecture 5
• Lecture 6
• Lecture 7

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

• Exercices on PCA

Data files

• Globines distances
• Globines List

Document and Links

Specific reference

• BIC criterion derivation

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.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/