Telecommunication Engineering

The course introduces the theory and design of machine learning algorithms, both in a supervised context (k-nn, neural networks, support vector machines) and, in a smaller measure, in an unsupervised context (k-means, gaussian mixture models, self-supervised learning). Students will acquire familarity with a variety of practical machine learning algorithms in both supervised and unsupervised scenarios, as long as a knowledge of the main prerequisites for their understanding (linear algebra, probability, optimization). The course is paired with a few practical laboratories where the students will learn how to implement all the techniques seen in class.