The course is included in these curricula and study modules

The course takes place in period

3 (2024-01-01 to 2024-03-17)

Level/category

Professional studies

Teaching language

English

Type of course

Compulsory

Cycle/level of course

Second

Recommended year of study

1

Total number of ECTS

5 cr

Competency aims

The aim of the course is that the student will be
able to apply data mining models and create a proper
pipeline for descriptive modelling of data.

Learning outcomes

Knowledge
At the end of the course, it is expected the student
to be able to understand the principles and
techniques of descriptive analytics and sequence
modelling with neural networks.

Course contents

The students learn to handle massive data
programmatically to perform feature engineering.
The students understand how to employ both
classification and clustering algorithms for
big data problems and how to utilize their output
in service creation. Students learn to carry out
verification of results as part of the solution
process.

Prerequisites and co-requisites

In is recommended to have basis of python and
statistics. Introduction to Analytics and Machine
Learning for Predictive problems courses are also
recommended.

Study activities

  • Lectures - 30 hours
  • Individual- and group instruction - 10 hours
  • Project- and production work/artistic activities - 30 hours
  • Individual studies - 65 hours

Workload

  • Total workload of the course: 135 hours
  • Of which autonomous studies: 135 hours
  • Of which scheduled studies: 0 hours

Mode of Delivery

Multiform education

Assessment methods

Essays, reports, productions and portfolio

Assessment requirements

To pass the course, the student should pass the
following examinations: Solution of a project in a
group where all concepts taught in the course are
applied.

Teacher

  • Espinosa Leal Leonardo
  • Scherbakov-Parland Andrej
  • Pham Truong An

Examiner

Espinosa Leal Leonardo

Home page of the course

Group size

No limit (31 students enrolled)

Assignments valid until

12 months after course has ended

Course enrolment period

2023-11-24 to 2023-12-22

Assessment methods

Date will be announced later - Reports and productions

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