Cancelled Advanced Topics in Machine Learning (ATML)
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 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, please, carefully 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.
MSc Programme in Computer Science
MSc Programme in Statistics
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
Lectures, class instructions and weekly home assignments.
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 offered by the Department of Computer Science (DIKU). In case you have not taken the “Machine Learning” course at DIKU, 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.
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 assessment5-7 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
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Class Instruction
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 1
- No limit
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Yevgeny Seldin (6-7b6d746c7176486c7136737d366c73)
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Courseinformation of students