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Machine Learning

Artificial Intelligence, Data Science, Computer Science, Mathematics, Statistics
Closed for application



Machine Learning (ML) is a subfield of artificial intelligence that is concerned with the design, analysis, implementation, and applications of programs that learn from examples or experience. Learning from data is commercially and scientifically important. ML consists of methods and respective software that extract automatically interesting knowledge (patterns, models, relationships) in large databases of sometimes chaotic and redundant information. ML is a data-based knowledge-discovering process that has the potential not only to analyze events in retrospect but also to predict future events or important alterations.

The objective of this module is to introduce the students to the most typical algorithms for data classification, regression and clustering. The subject has a strong technical focus and through learning by doing the students will advance this top field in computer science with growing job options.


» Learn the fundamentals of machine learning – regression, classification, clustering, short introduction to deep learning.

» Build and optimize (linear, non-linear) data representation models. Model performance and comparison.

» Learn to implement machine learning algorithms in software programs.

» Apply machine learning algorithms in practical problems with different nature.

» Define the problems in machine learning terminology and framework and chose the most relevant approach.

Module Level: Introduction

Degree level: Master

Language: English

Study mode: Online

SDG11 Theme: Machine Learning

Open to Life Long Learners: Yes


Semester: Spring

Starting date: May 2022

Finishing date: June 2022

Enrollment period: Until 11 April 2022

Participation Form: Fill in the application form available here. For further information please send an email to the Admission Office.


University of Aveiro

Participation info

Please fill the form given above. For futher information, please contact the Admissions Office through