Mathematical Aspects of Machine Learning

View the Project on GitHub jeremy9959/Math-5800-Spring-2020


  1. LeCun, Bottou, et. al. Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, November, 1998.
  2. LeCun, Cortes, Burges. The MNIST Database of handwritten digits
  3. Fokoue, Ernest. Model Selection for Optimal Prediction in Statistical Machine Learning, Notices of the AMS, March, 2020.
  4. Elkan, C. Maximum Likelihood, Logistic Regression, and Stochastic Gradient Training
  5. Logistic Regression for Classification from the Coursera Machine Learning Course.
  6. Sparse Logistic Regression from scikit-learn documentation.
  7. How the backpropagation algorithm works, Chapter 2 in Neural Networks and Deep Learning by Michael Nielsen.
  8. Janssens, Martens. Reflection on modern methods: Revisiting the area under the ROC curve, International Journal of Epidemiology, January, 2020.
  9. Bell, R., Yehuda Koren and Chris Volinsky. Matrix Factorization Techniques for Recommender Systems, in Computer, vol 42, pp. 30-37, August 2009.
  10. Matrix Factorization with Tensorflow, by Katherine Bailey.
  11. Goh, G. Why Momentum Really Works
  12. Goodfellow, I. et. al.Generative Adversarial Nets
  13. Zeiler, M. D. and Fergus, R. Visualizing and understanding convolutional networks