EE4C12 Machine learning for Electrical Engineering

Topics: Introduction at MSc level

This course teaches students the fundamentals of Machine Learning and how these can be applied to electrical engineering applications. After this introduction to Machine learning covering the fundamentals, the students can make use of this course by either taking advanced ML courses or by applying their learnings to detailed track courses.

The course uses the same book as EE2ML1 but goes more in-depth.

Course Contents

  1. Introduction to data analytics
  2. Regression (+intro Kernel methods)
  3. Classification
  4. Develop an ML workflow
  5. Feature engineering, selection
  6. Neural Networks
  7. Deep Learning
  8. Tree-based methods
  9. Reinforcement learning
  10. Hardware in ML

Study Goals

After this course, students will be able to 

  1. Draw conclusions by analysing data in the context of electrical engineering
  2. Compare machine learning concepts
  3. Use scikit-learn package in Python
  4. Apply training strategies for machine learning models to electrical engineering problems
  5. Design a workflow on an electrical engineering example


Machine Learning Refined, Foundations, Algorithms, and Applications” by Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos, Cambridge University Press; 2nd edition; 2020; ISBN: 9781108480727.


Jochen Cremer Justin Dauwels

Machine learning, with applications to autonomous vehicles and biomedical signal processing

PP Vergara Barrios

Last modified: 2023-11-03


Credits: 5 EC
Period: 4/0/0/0
Contact: Justin Dauwels