Unit VII – Elements and application of deep learning

Italian version

Lecturer: Giosuè Lo Bosco

The course presents neural networks as machine learning models for supervised and unsupervised classification of data. The first part of the course will deal with the general topic of machine learning, introducing some of the classic models for machine learning. Furthermore, the theoretical prerequisites for understanding neural models will be introduced. Subsequently, shallow and deep network architectures such as feed-forward, convolutive, recurrent, auto-encoders will be introduced. Finally, practical examples of neural networks applications will be provided in the context of forecasting in time series, including the problem of forecasting the stock market prices. The applications will be developed through the MATLAB scientific computing environment.