We introduce the principles and the critical analysis of the main paradigms for learning from data and their applications. The course provides the Machine Learning basis for both the aims of building new adaptive Intelligent Systems and powerful predictive models for intelligent data analysis.

Syllabus

- Computational learning tasks for predictions, learning as function approximation, generalization concept.
- Linear models and Nearest-Neighbors (learning algorithms and properties, regularization).
- Neural Networks (MLP and deep models, SOM).
- Probabilistic graphical models.
- Principles of learning processes: elements of statistical learning theory, model validation.
- Support Vector Machines and kernel-based models.
- Introduction to applications and advanced models.
- Application project: implementation and use of ML/NN models with emphasis to the rigorous application of validation techniques.