Single-Course English 2.5 ECTS

Advanced Topics in Machine Learning

Overall Course Objectives

To introduce the student to new trends in statistical signal processing and machine learning.

Learning Objectives

  • Comprehend and apply advanced methods within machine learning
  • Collect scientific knowledge and data related to topics covered in the course
  • Formulate and carry out a mini-project related to one or more of the covered course topics (preferably within the scope of the student’s PhD project)
  • Design a complex machine learning system based on an analysis of the problem and the project aims
  • Implement the machine learning system
  • Evaluate the performance of the machine learning system
  • Assess and summarize the mini-project results in relation to aims, methods and available data
  • Disseminate the project results in a technical report

Course Content

The course introduces new trends and advanced topics in machine learning. The course covers key topics in machine learning such as Bayesian parametric and non-parametric inference, optimization, latent variable models, kernel methods, and deep learning. The course consists of lectures and exercises, and is followed up by a mini-project presented in a written report. We encourage that students apply the methods taught to data relevant for their PhD project. Typical applications include: Bio-medical, audio, multimedia, and topic modeling as well as collaborative filtering and monitoring systems.

Teaching Method

Lectures, exercises, mini-project.


See course in the course database.





3 weeks




DTU Lyngby Campus

Course code 02901
Course type PhD
Semester start Week 32
Semester end Week 34
Days Mon-fri 8:00-17:00

10.600,00 DKK