Kies de Nederlandse taal
Course module: 202200021
Machine Learning for Datatypes
Course info
Course module202200021
Credits (ECTS)3.5
Course typeStudy Unit
Language of instructionEnglish
Contact persondr. F.A. Bukhsh
N. Bouali
Contactperson for the course
dr. F.A. Bukhsh
dr. F.A. Bukhsh
Examiner M. van Keulen
Contactperson for the course M. van Keulen
Academic year2022
Starting block
RemarksPart of TCS elective module 8E. Minor students: register for the minor!
Application procedure-
Registration using OSIRISNo
The module’s learning objectives are formulated using Bloom’s taxonomy. The learning objectives are mapped to Programme Intended Learning Outcomes.
  • Understand the data analytics workflow (based on CRISP-DM (Wirth, & Hipp, 2000))
  • Assess data quality and ability to scrape, cleanse, and ethically maintain data
  • Assess and compare the suitability of different data modeling methods/algorithms for optimal performance and evaluate results objectively.
  • Familiarity with infrastructures and distributed systems used to deal with them, such as Hadoop and MapReduce
  • Analyze and apply the most advanced and relevant statistic and mathematical techniques for business purposes, specifically
  • Fundamental algorithms and mathematical models for processing natural language
  • The fundamentals of the neural network as applied to the analysis of images
  • Mathematical methods of decision analysis and modeling ML/AI algorithms
  • Apply new frameworks and advanced fundamental knowledge, reflect on how frameworks work and motivate choice, integrate different parts of ML/AI.
  • Build an automated workflow to scrap, clean, and ethically maintain data and result’s privacy.
Working with data beyond simple structured data such as natural language text, images, and sensor data, as well as different neural network architectures typically used in machine learning for these data types.


Enrolment for this module is now closed. If you are not yet enrolled but wish to participate, contact the module co-ordinator Maurice van Keulen, 

B-CS students register via Osiris; others can contact
Minor students: please register in Osiris for the minor!
Assumed previous knowledge
It is a prerequisite to have made a serious attempt at module 202000991 Intelligent Interaction Design or 202001031 Intelligent Interaction Design CS/BIT. A ‘serious attempt’ is defined as having participated in at least one test, i.e., one need not have successfully completed the module, but the module expects that the content of one of these modules has been studied.
Previous knowledge can be gained by
All reading materials are accessible online either publicly or through the UT library.
Module 8E
Participating study
Bachelor Technical Computer Science
Participating study
Bachelor Business & IT
Participating study
Bachelor Creative Technology
Required materials
Recommended materials
Instructional modes
Presence dutyYes

Presence dutyYes



Presence dutyYes

Project supervised

Project unsupervised


Machine Learning for Datatypes

Kies de Nederlandse taal