Content, progress and pedagogy of the
The module adds to the knowledge obtained in 1st
- 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
- 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
- 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
- Design and develop autonomous systems that exploit machine
learning and big data.
- 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
- 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.
|Name of exam||Machine Learning and Big Data|
|Type of exam|
Written or oral exam
|Assessment||7-point grading scale|
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination
Policies and Procedures|