Machine Learning

About the course:

Successful organisations or companies like Google and Amazon have invested huge amounts in machine learning, which they use to analyze their customers' interests and behaviors to optimize their products, processes and marketing

In this course, you will learn to master the essential machine learning models in Matlab, Python, or R.

The course is based on the most popular machine learning course in Denmark including the textbook: "Introduction to Machine Learning and Data Mining". The course is given in English and is taught by DTU Associate Professors Morten Mørup and Mikkel N. Schmidt who have 10+ years of experience in machine learning research.

This course provides:
  • A strong intuition of different machine learning algorithms and knowledge of which methods you can apply on a given problem.
  • Skills in specific topics like model construction (feature extraction, dimensionality reduction, cross-validation, and model selection); supervised learning (linear regression, logistic classification, decision trees, artificial neural networks, and ensemble learning); and unsupervised learning (hierarchical clustering, kernel density estimation, mixture modeling, association mining, and outlier detection).
  • Insight into the most important steps in machine learning, from data preparation, modelling, validation and presentation of the obtained results.
  • Our machine learning book and access to our custom developed toolboxes in Python, Matlab, and R, which provides fast development, application and validation of the methods taught in the course.
  • The possibility for certification through report work, where the methods taught in the course are applied on your own data and problems.


This course is for:

Someone with an interest in machine learning and is comfortable with math as taught during first year of university studies (basic linear algebra and probability theory), and is ready to use and create machine learning algorithms.

  • Linear algebra as covered in this study material
  • Basic probability theory study material
  • Basic programming skills in either Matlab, Python, or R


More about the course and practical information is found here.


man 16 maj 22 9:00 -
fre 20 maj 22 16:30


DTU Compute


DTU Building 101, meeting center room 09, Kgs. Lyngby