Machine Learning for SCIENCE (MLS)
PhD School at the Faculty of SCIENCE at University of Copenhagen
This is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come, first-served basis and according to the applicable rules.
Anyone can apply for the course, but if you are not a PhD student at a Danish university (except CBS), you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to applicants on the waiting list.
Aim and Content
The Machine Learning for SCIENCE (MLS) course introduces key analysis methods in Machine Learning. These methods allow investigations of scientific data from most fields, including data from physical measurements, questionnaires, pictures, internet searches, satellites, and biochemical analyses. We cover data cleaning (e.g. missing data, denoising), feature extraction, machine learning basics (labels, variables, parameter optimization, overfitting, cross-validation), key machine learning and image analysis methods based on both unsupervised and supervised learning, and visualization. Method-wise, we start at Linear Discriminant Analysis and end with Deep Learning.
At the end of the course, the students must write a report with a suggestion for an analysis ideally performed on their own research data including a small implementation of a key concept. This report could form the basis for the Data Science Projects PhD course also offered by the Data Science Lab.
Learning outcomes
Intended learning outcome for the students who complete the course:
Knowledge
• Understand key machine learning concepts (e.g. parameter training, overfitting).
• Understand key machine learning methods (e.g. LDA, supervised learning).
• Understand key data analysis methods (e.g. feature extraction).
Skills
• Develop/adapt/extend a computer-based software method for analysis of relevant data.
Competences
• Propose relevant analysis methods for scientific data science problems.
• Consider cross-disciplinary data science methods in their research.
Target Group
PhD students from all SCIENCE departments with an element of data science in their research project.
Recommended Academic Qualifications
We use Python for the examples and exercises, so a basic level of Python programming experience is needed.
The Python skills could come from the Python for SCIENCE PhD toolbox course.
Research Area
All SCIENCE research fields, and secondarily other scientific fields with a data science element (e.g. health sciences).
Teaching and Learning Methods
The course is composed of sessions combining lectures and exercises.
For each topic, the students will get hands-on experience in applying, modifying, and programming analysis methods.
The programming examples will be implemented using Python in JupyterLab notebooks.
Type of Assessment
The students need to hand in their reports (10 days after the final course day) that must be approved. The students are allowed to work in 2-person groups.
Literature
Course lecture slides and exercises.
We will use data, examples, and other material from publicly available sources.
Course coordinator
Erik Dam, Professor, erikdam@di.ku.dk
Dates
2026: Apr 28, May 5, May 12, May 19, May 26.
Course location
Physically on campus.
Typically, at Nørre Campus, alternatively at Frederiksberg Campus.
Course fee
• PhD student enrolled at SCIENCE: 0 DKK
• PhD student from Danish PhD school Open market: 0 DKK
• PhD student from Danish PhD school not Open market: 3600 DKK
• PhD student from foreign university: 3600 DKK
• Master's student from Danish university: 0 DKK
• Master's student from foreign university: 3600 DKK
• Non-PhD student employed at a university (e.g., postdocs): 3600 DKK
• Non-PhD student not employed at a university (e.g., from a private company): 10.080 DKK
Cancellation policy
• Cancellations made up to two weeks before the course starts are free of charge.
• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000
• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000
• No-show will result in a fee of DKK 5.000
• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000
Course fee and participant fee
PhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.
In addition to the course fee, there might also be a participant fee.
If the course has a participant fee, this will apply to all participants regardless of participant
type - and in addition to the course fee.