Content, progress and pedagogy of the module

The module adds to the knowledge obtained in 1^st Semester.

Learning objectives

Knowledge

• Of the most important machine learning techniques.
• About tools for applying machine learning solutions.
• About characteristics of big data.
• About programming models and tools for big data analysis.

Skills

• Understand the types of machine learning algorithms, such as supervised, unsupervised and reinforcement learning.
• Understand the different classes of tasks where machine learning can be applied, including classification, regression and clustering problems.
• Apply machine learning algorithms in a given problem.
• Understand big data characteristics, such as volume, velocity, variety, veracity, valence, and value and explain how they can influence big data analysis.
• Create data models that suit the characteristics of given data.
• Design and develop autonomous systems that exploit machine learning and big data.

Competences

• Is able to compare, choose, or develop the most appropriate machine learning algorithm in a given problem.
• Can identify the type of task and required machine learning algorithm in a given application.
• Can identify what are big data problems.
• Must have the competency to compare and choose the most appropriate data model that suits the characteristics of given data.
• Is able to compare and assess the use of techniques and tools for issues that include collecting, storing, organizing, analyzing and using big data.

Type of instruction

The teaching is organized in accordance with the general form of teaching. Please see the programme cirruculum §17.

Extent and expected workload

Since it is a 5 ECTS course module the expected workload is 150 hours for the student.

Exam

Exams