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