ARTIFICIAL INTELLIGENCE: MACHINE LEARNING AND PATTERN RECOGNITION
|Academic year||2019/2020 Syllabus of previous years|
|Official course title||ARTIFICIAL INTELLIGENCE: MACHINE LEARNING AND PATTERN RECOGNITION|
|Course code||CM0472 (AF:306568 AR:166135)|
|Modality||On campus classes|
|ECTS credits||6 out of 12 of ARTIFICIAL INTELLIGENCE|
|Degree level||Master's Degree Programme (DM270)|
|Educational sector code||INF/01|
1.1. acquire the main models and algorithms of machine learning and pattern recognition
2. Ability to apply knowledge and understanding
2.1. acquire the ability to apply the studied models to real problems;
2.2. acquire the ability to critically assess the performance and the behavior of a model applied to a concrete problem
3.1. ability to understand which characteristics of the various models of artificial intelligence are best suited to a given problem;
3.2. ability to critically evaluate the theoretical characteristics of the proposed models;
Learning and inference in neural networks: feed-forward networks, deep learning architectures and related models.
Unsupervised and semi-supervised learning: K-means, spectral clustering, dominant sets, game-theoretic models.