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.
MSc Programme in Computer Science
MSc Programme in Statistics
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.
- 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 curriculumThe 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 (4-6571776544686d326f7932686f)
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