Introduction to Machine Learning

Course Content - Continuing Education

The course is designed around the above data modeling framework where the different lectures and exercises will focus on an aspect of the data modeling framework

Time and Place: The course will take place on 5. - 9. March 2018 at DTU in Lyngby.

Price: 20,000 DKK excluding VAT. The textbook of the course will be free.

Course registration send an email to

Contact person: Morten Mørup (, Mikkel N. Schmidt (

What can you learn from your data?
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).

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.

This course builds on the successful DTU course and textbook: "Introduction to Machine Learning and Data Mining", and is taught by DTU Associate Professors Morten Mørup and Mikkel N. Schmidt who have 10+ years of experience in machine learning research.

Target audience: The target audience is anyone who is interested in machine learning, who is comfortable with 1st year university math (linear algebra and basic probability) and ready to endeavor into creating and programming machine learning algorithms.

Prerequisites: Linear algebra as covered in this study material.

Basic probability theory as covered in this study material.

Basic programming skills in either Matlab, Python, or R.

For course schedule and further details, click here.

Language: English


Morten Mørup
DTU Compute
45 25 39 00


Mikkel Nørgaard Schmidt
Associate Professor
DTU Compute
45 25 52 70