Statistical Methods for Machine Learning

A.Y. 2020/2021
6
Max ECTS
48
Overall hours
SSD
INF/01
Language
English
Learning objectives
The course describes and analyzes, in a rigorous statistical framework, some of the most important machine learning techniques. This will provide the student with a rich set of conceptual and methodological tools for understanding the general phenomenon of learning in machines.
Expected learning outcomes
Upon completion of the course students will be able to:
1. understand the notion of overfitting and its role in controlling the statistical risk
2. describe some of the most important machine learning algorithms and explain how they avoid overfitting
3. run machine learning experiments using the correct statistical methodology
These objectives are measured via a combination of two components: the project report and the oral discussion. The final grade is formed by assessing the project report, and then using the oral discussion for fine tuning.
Single course

This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.

Course syllabus and organization

Single session

Lesson period
Second semester
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor(s)
Reception:
By appointment
18, via Celoria. Room 7007