Machine Learning Algorithms from a Mathematical Perspective

Madan Pal

doi:10.55544/jrasb.3.3.40

Authors

Madan Pal Assistant professor Department of Mathematics, Vijay Singh Pathik Government (PG) College Kairana, Shamli, Uttar Pradesh, INDIA.

DOI:

https://doi.org/10.55544/jrasb.3.3.40

Keywords:

Machine learning, Optimization, Models, Learning

Abstract

Machine learning (ML) has become a cornerstone of modern technological advancement, contributing significantly to fields such as artificial intelligence, data science, computer vision, natural language processing, and robotics. The growing success of machine learning can be attributed to the development of powerful algorithms that leverage vast amounts of data to automatically identify patterns and make predictions. These algorithms have demonstrated remarkable efficacy in a wide array of real-world applications, from image classification to speech recognition and beyond. While machine learning’s practical impact is undeniable, a deep understanding of the mathematical principles behind these algorithms is crucial for improving their efficiency, interpretability, and generalization capabilities. By analyzing machine learning from a mathematical perspective, we gain insight into the strengths, limitations, and potential improvements of these models, ensuring their continued success and ethical application.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

He, K. (2020). Machine Learning for Finance. Springer.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R. Springer.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

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

Ng, A. Y. (2004). Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proceedings of the Twenty-First International Conference on Machine Learning (p. 78).

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144).