The course is included in these curricula and study modules
- Big data analytics 2016 - Big data analytics
- Big data analytics 2017 - Big data analytics
- Big data analytics 2018 - Big data analytics
- Big data analytics 2019 - Big data analytics
- Big data analytics 2020 - Big data analytics
- Big data analytics 2021 - Big data analytics - specialised professional studies
- Big data analytics 2022 - Big data analytics - specialised professional studies
- Big data analytics 2023 - Big data analytics - specialised professional studies
The course takes place in period
3 (2023-01-01 to 2023-03-19)
Level/category
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
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
Basic python programming skills, statistics and linear algebra are required. Introduction to Analytics and Machine Learning for Predictive problems courses are also recommended. Basis of calculus is recommended but not required.
Recommended or required reading
Leskovec, J., Rajaraman, A., & Ullman, J. D. (2020). Mining of massive data sets. Cambridge university press.
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
- Majd Amin
- Scherbakov-Parland Andrej
Examiner
Espinosa Leal Leonardo
Home page of the course
Group size
No limit (26 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2022-12-12 to 2023-01-12
Assessment methods
Date will be announced later - Reports and productions
| Date | Time | Room | Title | Description | Organizer |
|---|---|---|---|---|---|
| 2023-01-19 | 13:00 - 18:00 | D4109 | Machine Learning for Descriptive Problems | Espinosa Leal Leonardo Scherbakov-Parland Andrej |
|
| 2023-01-20 | 13:00 - 18:00 | D4109 | Machine Learning for Descriptive Problems | Espinosa Leal Leonardo Scherbakov-Parland Andrej |
|
| 2023-02-02 | 13:00 - 18:00 | D4109 | Machine Learning for Descriptive Problems | Espinosa Leal Leonardo Scherbakov-Parland Andrej |
|
| 2023-02-03 | 13:00 - 18:00 | D4109 | Machine Learning for Descriptive Problems | Espinosa Leal Leonardo Scherbakov-Parland Andrej |
|
| 2023-02-16 | 13:00 - 18:00 | D4109 | Machine Learning for Descriptive Problems | Espinosa Leal Leonardo Scherbakov-Parland Andrej |
|
| 2023-02-17 | 13:00 - 18:00 | D4109 | Machine Learning for Descriptive Problems | Espinosa Leal Leonardo Scherbakov-Parland Andrej |