Machine Learning

Today's leading companies and organizations are using machine learning to analyze their data and optimize their processes in almost all areas of business. In this course, you will learn to master the essential machine learning models in Matlab, Python, or R.

You will develop a strong intuition of different machine learning algorithms, and decide which methods to apply on a given problem. You will become proficient 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).

What can you learn from your data?

In particular, the course will establish the major steps in any machine learning pipeline from preparing the data, to modeling the data and disseminating the results, and throughout the course we encourage participants to apply the methods learned on their own data and problem domains.

Read more and submit the course via this link.



Pia Lauridsen
DTU Compute
+45 45 25 37 23


Mikkel Nørgaard Schmidt
Associate Professor
DTU Compute
+45 45 25 52 70


Morten Mørup
Associate professor
DTU Compute
+45 45 25 39 00