Machine Learning and Optimisation

Machine Learning and Optimisation #

Coure Description on UvA’s website

Topics #

This course will be a mix of machine learning theory and applications of this knowledge on large datasets.

Topics include:

Support vector machine;
Neural networks, deep learning, and stochastic gradient descent;
Boosting, column generation, and margin maximisation;
Bagging, random forest, optimal decision trees, and mixed-integer optimisation.

Teaching materials #

Online reader MachineLearningTheory.org
Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer, New York, ISBN 0387310738; see also https://www.microsoft.com/en-us/research/people/cmbishop/
Bertsimas, D., & Dunn, J. (2019). Machine Learning Under a Modern Optimisation Lens. Massachusetts: Dynamic Ideas LLC, First Edition, ISBN 1733788506;