Machine learning is rapidly becoming a crucial paradigm for the solution of complex automation problems.

This course provides a general introduction to  machine learning in which both classical models and recent advancements are presented.

The first part of the course covers basic definitions, classic techniques and learning algorithms.
The second part focuses on artificial neural networks and deep learning.
The third part discusses some notable examples of applications such as image recognition, natural language processing and recommender systems.

About two thirds of the course are given as lectures in which machine learning principles and techniques are presented. One third of the course takes place in a laboratory where the studentslearn to solve machine learning problems by using the Python programming language.