DM894: Advanced topics in data mining and machine learning
The Study Board for Science
Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340131112, N340131102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Autumn
Level: Master
STADS ID (UVA): N340131101
ECTS value: 10
Date of Approval: 02-03-2023
Duration: 1 semester
Version: Approved - active
Entry requirements
Academic preconditions
Students taking the course are expected to have a basic knowledge of machine learning methods, roughly corresponding to that obtained through following DM566, DS804, DM870, or similar courses, probability theory, and statistics as well as the skills to work with such subjects in practice in preferably the Python language.
Course introduction
The aim of the course is to enable the student to gain an understanding of advanced methods in machine learning and data mining which is important in regard to staying up-to-date with the latest research and industry trends in the field.
The course builds on the knowledge acquired in courses such as DM566, DS804, and DM870, and gives an academic basis for studying advanced machine learning and data mining topics.
In relation to the competence profile of the degree, it is the explicit focus of the course to:
The course builds on the knowledge acquired in courses such as DM566, DS804, and DM870, and gives an academic basis for studying advanced machine learning and data mining topics.
In relation to the competence profile of the degree, it is the explicit focus of the course to:
- Prepare for master's project in machine learning and data mining topics.
- Give the competence to plan and carry out scientific projects at a high professional level.
- Give skills to describe, analyze, and solve advanced computer science problems using the learned methods.
- Give knowledge and understanding of specialized models and methods developed in computer science based on the highest international research.
Expected learning outcome
The goal of the course is to introduce students to advanced machine-learning methods and tools that are at the forefront of research and industry interests. The participants should also gain further programming experience by applying these advanced concepts to small practical problems.
The course builds on the acquired knowledge in mathematics and programming and gives an academic basis for studying the topics selected for the course.
After the course, the student is expected to be able to:
- Plan and carry out scientific projects at a high professional level.
- Describe, analyze and solve advanced computer science problems using the learned methods.
- Develop new variants of the learned methods where the specific problem requires it.
- Describe novel computer science models and methods and their intended application in other professional areas.
- The student can demonstrate knowledge of a selection of specialized models and methods developed in computer science based on the highest international research, including topics from the subject's research front.
Content
The course is a seminar course where the participants sign up for different subjects supervised by different teachers, plan and conduct independent literature surveys, and present their findings to an audience of peers. This format allows the students to develop academic skills in project planning, presenting, and scientific writing as they dive deep into the academic literature. The focus of this course is on advanced methods in machine learning, where the students will work on subjects such as generative modelling, ensemble tree structures, and disclosure-resistant neural network models.
Keywords include but are not limited to: Generative adversarial networks, autoencoders, synthetic data evaluation, information disclosure, privacy evaluation metrics, multivariate comparison.
Keywords include but are not limited to: Generative adversarial networks, autoencoders, synthetic data evaluation, information disclosure, privacy evaluation metrics, multivariate comparison.
Literature
Examination regulations
Prerequisites for participating in the exam a)
Timing
Autumn
Tests
Oral presentation
EKA
N340131112
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Student Identification Card
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
0
Additional information
Exam consists of an oral presentation of an assigned topic
Exam element a)
Timing
Autumn
Tests
Project report
EKA
N340131102
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
10
Additional information
Exam consists of a project report based on individually assigned topics.
Indicative number of lessons
Teaching Method
The course is a seminar course where the participants sign up for different subjects supervised by different teachers, plan and conduct independent literature surveys, and present their findings to an audience of peers. The intro phase is divided between the initial introductions by the teachers and the topics introduced by the students according to the assignments. The training phase consists of supervision regarding the presentations and the project. The study phase consists of preparing the presentations and executing the projects.
Teacher responsible
Additional teachers
| Name | Department | City | |
|---|---|---|---|
| Anton Danholt Lautrup | lautrup@imada.sdu.dk | Data Science | |
| Tobias Hyrup | hyrup@imada.sdu.dk | Data Science |
Timetable
Administrative Unit
Team at Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.