Topics in machine learning

Fabio Aiolli
Department of Mathematics  

The aim of the course is to introduce the student to the basic concepts that characterize machine learning, i.e. the class of techniques and algorithms that starting from empirical data allow a computer system to acquire new knowledge, or to correct and/or to refine knowledge already available. These techniques are particularly useful for problems for which it is impossible or very difficult to reach a mathematical formalization usable for the definition of an ad hoc algorithmic solution. Examples of these problems are perceptual tasks, such as visual recognition of handwritten digits, or problems in which data is corrupted by noise or is incomplete.

- Introduction: when machine learning is useful; machine learning paradigms; basic ingredients of machine learning. Examples of applications.
- Supervised Learning (SL): Neural Networks and Support Vector Machines. Learning Theory. The Representation problem. Kernel and feature learning. Evaluation measures.

Course requirements
The student should be familiar with basic concepts in probability and calculus of multivariate functions. It is also advisable to have basic knowledge of programming.


19 June, 14:30-17:30
21 June, 14:30-17:30

Meeting room at HIT Center, Via Luzzatti 4

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