Advanced Topics in Machine Learning (ATML)

Course content

The purpose of the course is to expose students to selected advanced topics in machine learning. The course will bring the students up to a level sufficient for writing a master thesis in machine learning.

The course is relevant for computer science students as well as students from other studies with a good mathematical background, including Statistics, Actuarial Mathematics, Mathematics-Economics, Physics, etc. We assume that the students have previously passed the Machine Learning master course or Machine Learning A+B courses offered by DIKU.

The exact list of topics will depend on the lecturers and trends in machine learning research and will be announced on the course's Absalon page. Feel free to contact the course organiser for details.


WARNING: If you have not taken DIKU's Machine Learning master course or DIKU's Machine Learning A+B courses, please, check the "Recommended Academic Qualifications" box below and the self-preparation assignment at https:/​/​​​machine-learning-courses/​atml. Machine Learning courses given at other places do not necessarily prepare you well for this course. It is not advised taking the course if you do not meet the academic qualifications.



MSc Programme in Computer Science

MSc Programme in Statistics

Learning outcome

Knowledge of

Selected advanced topics in machine learning, including:

  • design of learning algorithms
  • analysis of learning algorithms

The exact list of topics will depend on the teachers and trends in machine learning research. They will be announced on the course's Absalon page.

Skills to

  • Read and understand recent scientific literature in the field of machine learning
  • Apply the knowledge obtained by reading scientific papers
  • Compare machine learning methods and assess their potentials and shortcomings

Competences to

  • Understand advanced methods, and apply the knowledge to practical problems
  • Plan and carry out self-learning

Lectures, class instructions and weekly home assignments.

See Absalon.

The course requires a strong mathematical background. It is suitable for computer science master students, as well as students from mathematics (statistics, actuarial math, math-economics, etc) and physics study programmes. Students from other study programmes can verify if they have sufficient math and programming skills by solving the self-preparation assignment (below) and if in doubt contact the course organiser.

It is assumed that the students have successfully passed the “Machine Learning” course or Machine Learning A+B courses offered by the Department of Computer Science (DIKU). In case you have not taken them, please, go through the self-preparation material and solve the self-preparation assignment provided at https:/​/​​​machine-learning-courses/​atml before the course starts. (For students with a strong mathematical background and some background in machine learning it should be possible to do the self-preparation within a couple of weeks.) It is strongly not advised taking the course if you do not meet the prerequisites.

Programming Language: The programming language of the course is Python. The self-preparation assignment includes a few programming tasks; if you can code them in Python, you should be fine.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
5-7 weekly take-home assignments. The assignments must be solved individually.

The course is based on weekly home assignments, which are graded continuously over the course of the semester. The final grade is given as a weighted average of the assignments, except the assignment with the poorest assessment.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

The re-exam consists of two parts:

1. The first part is handing in at least 5 of the course assignments no later than 2 weeks before the oral part of the re-exam
2. The second part is a 30 minutes oral examination without preparation in the course curriculum

The final grade will be given as an overall assessment of the two re-exam parts.

Criteria for exam assessment

See Learning Outcome.


Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Class Instruction
  • 14
  • Preparation
  • 70
  • Exercises
  • 94
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 1
No limit
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Saeed Masoudian   (15-7c6a6e6e6d37766a7c787e6d726a77496d7237747e376d74)

Christian Igel

Saved on the 25-07-2023

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students