NDAK15014U Cancelled Advanced Topics in Machine Learning (ATML)

Volume 2022/2023
Education

MSc Programme in Computer Science

MSc Programme in Statistics

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:/​/​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.

 

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

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:/​/​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.
Lectures, class instructions and weekly home assignments.
  • Category
  • Hours
  • Lectures
  • 28
  • Class Instruction
  • 14
  • Preparation
  • 70
  • Exercises
  • 94
  • Total
  • 206
Written
Continuous feedback during the course
Credit
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.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

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.