This course will cover fundamental concepts in pattern recognition and machine learning. We will focus on mathematical formulations and computational methods that are broadly applicable. Topics include, but are not limited to, supervised learning, parametric and non-parametric models, decision theory, bayesian inference, dimensionality reduction, clustering, feature selection, generalization bounds, support vector machines and neural networks.
Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer, 2006. ISBN: 9780387310732.
Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014. ISBN: 9781107298019.