This course is an introduction to machine learning (ML), a field of research in artificial intelligence. It is taught by Prof. Ioannis Mitiliagkas.
The course will cover the following subjects: general notions (basic terminology, generalization, curse of dimensionality, capacity, classifier comparison), supervised algorithms (k-nearest neighbors, linear classifiers, neural networks, support vector machines, decision trees and regression, ensemble methods), unsupervised algorithms (principal component analysis, k-means method), overview of probabilistic graphical models.
For more info, see the class website.