Intro2StatisticalLearning/Ressources.qmd at main · ASPteaching/Intro2StatisticalLearning · GitHub

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---
title: "References and Ressources"
---

# References

## Tree and Ensemble based methods

- Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press.

- Brandon M. Greenwell (202) Tree-Based Methods for Statistical Learning in R. 1st Edition. Chapman and Hall/CRC DOI: https://doi.org/10.1201/9781003089032 Web site

- Efron, B., Hastie T. (2016) Computer Age Statistical Inference. Cambridge University Press. Web site

- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer.

- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Springer.

## Artificial Neural Networks

- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning (Vol. 1). MIT press. Web site

- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

- Chollet, F. (2018). Deep learning with Python. Manning Publications.

- Chollet, F. (2023). Deep learning with R . 2nd edition. Manning Publications.

# Some interesting online resources

## Statistical/Machine Learning in General

- [Google's Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)

- [David Wishart's Machine Learning Course @ Bioinformatics Canada](https://bioinformaticsdotca.github.io/MLE_2023)

- [Applied Data Mining and Statistical Learning (Penn Statte-University)](https://online.stat.psu.edu/stat508/)

- [R for statistical learning](https://daviddalpiaz.github.io/r4sl/)

- [Introduction to Statistical Learning (ISL)]()

## Decision Trees

- [Decision Trees free course (9 videos). By Analytics Vidhya](https://www.youtube.com/playlist?list=PLdKd-j64gDcC5TCZEqODMZtAotCfm5Zkh)

- [An Introduction to Recursive Partitioning Using the RPART Routines](https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf)

## Neural Networks and Deep Learning


- [MIT Deep Learning 6.S191](https://introtodeeplearning.com/)

- [Coursera: Neural Networs and Deep Learning (Andrew Ng)](https://www.coursera.org/learn/neural-networks-deep-learning)

- [Practical Deep Learning *for coders*](https://course.fast.ai/)

- [Deep Learning Course (François Fleuret)](https://fleuret.org/dlc/)

- [Deep Learning (1day) Workshop](https://rses-dl-course.github.io/#course-description)

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