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

• David Wishart’s Machine Learning Course @ Bioinformatics Canada

• Applied Data Mining and Statistical Learning (Penn Statte-University)

• R for statistical learning

• Introduction to Statistical Learning (ISL)

Decision Trees

• Decision Trees free course (9 videos). By Analytics Vidhya

• An Introduction to Recursive Partitioning Using the RPART Routines

Neural Networks and Deep Learning

• MIT Deep Learning 6.S191

• Coursera: Neural Networs and Deep Learning (Andrew Ng)

• Practical Deep Learning for coders

• Deep Learning Course (François Fleuret)

• Deep Learning (1day) Workshop

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