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README.Rmd
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README.Rmd
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---
output: github_document
editor_options:
markdown:
wrap: 72
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
# Statistical Learning, Machine Learning & Artificial Intelligence
*Overview*
The course introduces the most important algorithmic and statistic machine
learning tools. The first part of the course focuses on the statistical
foundations and on the methodological aspects. The second part is more
hands-on, with practical applications to help develop the necessary software
skills.
The course aims at teaching a methodological and practical overview to
statistical learning methods. The emphasis is on the applications and
state-of-the-art techniques are presented through hands-on tutorial
with **R**. The focus will be on business-oriented libraries allowing to
integrate statistical models into production-ready tools.
## General Information
- [Course Descriprion (for Students)](https://marcozanotti.github.io/statlearning-course/general-infos/statlearn_description.html)
- [Course Descriprion (for Business Experts)](https://marcozanotti.github.io/statlearning-course/general-infos/statlearn_description_business.html)
- [Syllabus](https://marcozanotti.github.io/statlearning-course/general-infos/statlearn_syllabus.html)
## Materials
- [Lectures](https://github.com/marcozanotti/statlearning-course/tree/master/R)
- [Lecture 0 - Tidyverse](https://marcozanotti.github.io/statlearning-course/R/statlearn_lecture0_tidyverse.html)
## Suggested References
R Programming:
- [R for Data Science](https://r4ds.had.co.nz/)
- [Efficient R Programming](https://csgillespie.github.io/efficientR/index.html)
- [R Packages](https://r-pkgs.org/index.html)
- [Advanced R](https://adv-r.hadley.nz/)
Statistics & ML:
- [Feature Engineering and Selection](https://www.tidymodels.org/books/fes/)
- [Tidy Modelling with R](https://www.tmwr.org/)
- [Stacking with R](https://stacks.tidymodels.org/index.html)
- [AutoML with H2O](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html)
- [Deep Learning with Keras](https://keras.rstudio.com/)
Everything with R:
- [Big Book of R](https://www.bigbookofr.com/)