The course is included in these curricula and study modules
- Information technology 2014 - Service oriented architectures and system design
- Information technology 2015 - Service oriented architectures and system design
- Information technology 2016 - Service oriented architectures and system design
- Information technology 2017 - Service oriented architectures and system design
- Information technology 2018 - Machine learning and decision support system development
- Information technology 2019 - Machine learning and decision support system development
- Information technology 2020 - Machine learning and decision support system development
- Information technology 2021 - Machine learning and decision support system development - advanced studies
- Information technology 2022 - Machine learning and decision support system development - advanced studies
- Information technology 2023 - Machine learning and decision support system development - advanced studies
The course takes place in period
3 (2024-01-01 to 2024-03-17)
Level/category
Teaching language
English
Type of course
Compulsory
Cycle/level of course
First
Recommended year of study
3
Total number of ECTS
5 cr
Competency aims
In this study unit the focus lies on the following
competencies:
Machine learning and decision support system
development with an emphasis on:
Analysis of unstructured data
Development of software applying relevant
algorithms in text analysis
SDG's in focus:
#4: Quality education
#9: Industry, innovation and infrastructure
Learning outcomes
Upon completion of this study unit:
You know the key issues and basic concepts,
methods and techniques of text analysis.
(Knowledge)
You can process natural languages in practice.
(Skills)
You can construct programs capable of processing
and analysing large amounts of natural language
data. (Skills)
You have an understanding of how text analysis can
be used to facilitate various everyday problems.
(Approach)
Prerequisites and co-requisites
Data processing
Recommended or required reading
Jurafsky, Dan, and James H. Martin. "Speech and
language processing. Vol. 3." US: Prentice Hall
(2014). (online copy available:
https://web.stanford.edu/~jurafsky/slp3/ External link )
Christopher D. Manning, Prabhakar Raghavan and
Hinrich Schütze. Introduction to information
retrieval. Vol. 39. Cambridge: Cambridge
University Press, 2008.
https://nlp.stanford.edu/IR External link-
book/html/htmledition/irbook.html
Study activities
- Lectures - 40 hours
- Project- and production work/artistic activities - 95 hours
Workload
- Total workload of the course: 135 hours
- Of which autonomous studies: 135 hours
- Of which scheduled studies: 0 hours
Mode of Delivery
Participation in tuition
Assessment methods
Course Project
Teacher
Dayama Niraj
Examiner
Dayama Niraj
Home page of the course
Group size
No limit
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 - Other assignments