Advanced Topics in Machine Learning (ATML)

Course content

The purpose of this 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 good mathematical background, including Statistics, Actuarial Mathematics, Mathematics-Economics, Physics, etc.

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.

Examples of topics that were taught in the last year include:

  • PAC-Bayesian analysis

    • The soul of Machine Learning: How to trade-off prior knowledge and data fit in a principled way

  • Stochastic Gradient Descent

  • Advanced topics on Support Vector Machines

    • Learn fast ways to train one of the most successful learning models – Support Vector Machines

  • Online learning
    • How to learn when data collection and learning are coupled together

    • How to adapt to changing and adversarial environments

  • Reinforcement learning

    • How to learn when agent actions are changing its state


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 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 programs. Students from other study programs are strongly advised to contact the course organiser to verify suitability of their background prior to signing up for the course.

It is assumed that the students have successfully passed the “Machine Learning” course offered by the Department of Computer Science (DIKU). In case you have not taken the “Machine Learning” course at DIKU, please, contact the course organiser to obtain the relevant material and do the necessary self-preparation before the beginning of the course. (For students with strong mathematical background and some background in machine learning it should be possible to do the self-preparation within a couple of weeks.)

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
5-7 weekly take home exercises.
The final grade will be the average over all assignments except the worst one.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

See Learning Outcome.


Single subject courses (day)

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