Advanced Topics in Machine Learning (ATML)

Course content

The purpose of the course is to expose students to selected advanced topics in theoretical machine learning. In particular, the course will focus on advanced theoretical tools for design and analysis of machine learning algorithms. We will also introduce differential privacy and differentially private learning algorithms. By the end of the course, students will be well-equipped to undertake a master's 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 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 A+B courses, please, check the "Recommended Academic Qualifications" box below. 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.

 

Education

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
  • design and analysis of private 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 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.

Written
Continuous feedback during the course of the semester
ECTS
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
6-8 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 assessment

See Learning Outcome.

 

Single subject courses (day)

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

Kursusinformation

Language
English
Course number
NDAK15014U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 1
Schedulegroup
C
Capacity
No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Amartya Sanyal   (4-67737967466a6f34717b346a71)
Saved on the 14-02-2024

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Courseinformation of students