Visualisation and dimension reduction

Outline

1. Factor Analysis
2. Variational Auto-encoder

• Neurons
• Variational EM (VEM)

3. Visualisation

• SVD
• t-SNE
• UMAP

4. Introduction to NLP

Reference document

The lecture closely follows and largely borrows material from “Machine Learning: A Probabilistic Perspective” (MLAPP) from Kevin P. Murphy, chapters:

Lectures Notes

• Factor Analysis
• Neurons
• Variational Auto Encoders
• Singular Value Decomposition

Document and Links

Reference books about machine learning

• Machine Learning: A Probabilistic Perspective from Kevin P. Murphy
• 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/