NDAK15014U Cancelled Advanced Topics in Machine Learning (ATML)
MSc Programme in Computer Science
MSc Programme in Statistics
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://sites.google.com/diku.edu/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.
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.
- 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
- Understand advanced methods, and apply the knowledge to practical problems
- Plan and carry out self-learning
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://sites.google.com/diku.edu/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.
- Class Instruction
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 7,5 ECTS
- Type of assessment
- Continuous assessment
- Type of assessment details
- 5-7 weekly take-home assignments. The assignments must be
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 assesment
See Learning Outcome.
- Course code
- 7,5 ECTS
- Full Degree Master
- 1 block
- Block 1
- Course capacity
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Yevgeny Seldin (6-75676e666b7042666b306d7730666d)