FOUNDATIONS OF MACHINE LEARNING

The course aims at introducing the student to the principles, the algorithms and the main applications of Artificial Intelligence, with a focus on inference, machine learning and pattern recognition.

1. Knowledge and understanding
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. Judgement
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;

The student is expected to be familiar with the basic concepts of calculus and probability theory.

Information theory and inference: source coding, channel coding.

Learning and inference in neural networks: feed-forward networks, deep learning and related models.

Statistical learning theory, support vector machines, and kernel methods.

Unsupervised and semi-supervised learning: K-means, spectral clustering, dominant sets, game-theoretic models.

D. J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.

I. Goodfellow, Y. Bengio and A. Courville. Deep Learning, MIT Press, 2016.

The exam consists of an oral test together with the discussion of a project agreed previously with the teacher.

Powerpoint presentations and chalk talk.

English

To favor an "active" appraoch to the study of the topics covered in the classes, students will be asked to develop a simple project which will be discussed during the oral examination.

oral