M692 (Spring 2002): Mathematics of Machine Learning
Resource List
One goal of the seminar is to identify and collect high-quality open resources. Please send additions to elbueler@alaska.edu. They need to be open resources, so please include a link.
- Bottou, L., Curtis, F. E., & Nocedal, J. (2018). Optimization methods for large-scale machine learning. SIAM Review, 60(2), 223--311.
- Bronstein, M. M., Bruna, J., LeCun, Y., Szlam A., & Vandergheynst, P., (2017). Geometric deep learning: Going beyond Euclidean data, IEEE Signal Processing Magazine 34(4), 18--42.
- Deisenroth, M. P., Faisal, A. A., & Ong C. S., Mathematics for Machine Learning, Cambridge University Press 2020.
- Goh, G. (2017). Why momentum really works, Distill.
- Goodfellow, I., Bengio, Y., & Courville, A., Deep Learning, MIT Press, 2016
- Hamilton, W., Graph Representation Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, Vol. 14, No. 3, Morgan & Claypool Publishers 2020.
- Higham, C. F., & Higham, D. J. (2019). Deep learning: An introduction for applied mathematicians. SIAM Review, 61(4), 860--891.
- Kingma, D. P. & Ba, Y. (2014). Adam: A method for stochastic optimization., arXiv preprint arXiv:1412.6980.
- LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature 521, 436--444.
- Reddi, S. J., Kale, S., & Kumar, S. (2019). On the convergence of Adam and beyond, preprint arXiv:1904.09237.
- Sanchez-Lengeling, B., Reif, E., Pearce, A., & Wiltschko, A. B., (2021). A gentle introduction to graph neural networks, Distill.
- Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2019). A survey of optimization methods from a machine learning perspective. IEEE Transactions on Cybernetics, 50(8), 3668--3681.
- Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent, Proceedings of the 20th International Conference on Machine Learning, 928-936.
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