In the Big Data era, Machine Learning and other Data Mining became critical paradigms in any application field. The lecture introduces the principles of Machine Learning, and the evolution to Deep Learning paradigms. It presents the methods of stochastic variational and Bayesian inference, kernel techniques, and is focusing on the modern methods and algorithms of Deep Learning. Since the data sets are organic part of the learning process, the dataset biases pose new challenges. The lecture addresses the topics of data bias, cross-dataset generalization, for very specific cases in applications with a large variability of the data semantic content. In the present context of the fantastic evolution of the quantum technologies, the lecture is looking at near future change of paradigm and is introducing the basics of quantum information processing and machine learning.
