Some Important Papers:

• Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems?. The journal of machine learning research, 15(1), 3133-3181.
• Fernández-Delgado, M., Sirsat, M. S., Cernadas, E., Alawadi, S., Barro, S., & Febrero-Bande, M. (2019). An extensive experimental survey of regression methods. Neural Networks, 111, 11-34.
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260: http://www.cs.cmu.edu/~tom/pubs/Science-ML-2015.pdf
• Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
• Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
• Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
• Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7, 53040-53065.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.