Machine Learning & Data Science II

Department
  • Bachelor's program Mechatronics
Course unit code
  • MECH-B-5-MLDS-MLDS2-ILV
Number of ECTS credits allocated
  • 5.0
Name of lecturer(s)
  • Kandolf Peter, PhD, McGuiness Daniel, PhD
Mode of delivery
  • face-to-face
Recommended optional program components
  • none
Recommended or required reading
  • - Subasi, A. (2020). Practical machine learning for data analysis using Python.


    - VanderPlas, J. (2016). Python data science handbook : essential tools for working with data (First edition.)


    - Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems (Second edition.).
Assessment methods and criteria
  • Course immanent examination
Level of course unit
  • Bachelor
Year of study
  • Fall 2025
Semester when the course unit is delivered
  • 5
Language of instruction
  • English
Learning outcomes of the course unit
  • • Know the basics of the Python programming language.
    • Know how to use libraries/packages and frameworks in Python that relate to Machine Learning and Data Science.
    • Know the basic mathematics behind Machine Learning
    • Know typical processes in Machine Learning
    • Know classical methods to generate data
    • Know how to evaluate a data set for its quality
    • Know basic network structures and classes
    • Know how to train networks
    • Understand methods for evaluating performance
    • Have basic knowledge regarding optimisation
    • Have knowledge concerning of Time Series Classification
    • Are able to use Image Processing with ML
Course contents
  • • Optimisation
    • Time Series Classification
    • Image Processing with Machine Learning
Planned learning activities and teaching methods
  • The course comprises an interactive mix of lectures, discussions and individual and group work.
Work placement(s)
  • none

Back