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Machine Learning and Imaging Projects
Second title: Machine Learning and Imigaing Projects
Provider: Department of Computer Science

Activity no.: 5173-20-02-32 
Enrollment deadline: 08/09/2020
PlaceUniversitetsparken 1
Universitetsparken 1, 2100 København Ø
Date and time22.09.2020, at: 00:00 - 24.11.2020, at: 00:00
Regular seats20
Course fee5,400.00 kr.
ECTS credits4.50
Contact personErik Bjørnager Dam    E-mail address: erikdam@diku.dk
Enrolment Handling/Course OrganiserErik Bjørnager Dam    E-mail address: erikdam@diku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 1
Exam requirementsThe students need to hand in their report and present their work at the final course day. Both report and oral presentation must be approved. The students are allowed to work and present in 2-person groups
Exam formA report on an agreed topic followed by an oral presentation
Exam formAndet/Other
Exam detailsThe students need to hand in their report (shortly after the final course day). The report must be approved. The students are allowed to work in 2-person groups.
Grading scalePassed / Not passed

With increasing interest in applying Machine Learning methods in different domains, this course provides a platform to develop and work on projects with the student’s own data using Machine Learning methods, with supervisory support from the course teachers. The data sources can range from pictures, scans, videos or graphs to tabulated measurements in text or digital formats; all related to some scientific investigation. Ideally, the size and complexity of the data is such that manual analysis is either tedious or not feasible to perform. Typical analysis within the scope of this course could be automated quantification of objects of interests in the data (for example, image analysis), or combining different types of data to address a common research question (like combining text and measurements), or building predictive models using Machine Learning. The course output will be a manuscript written like a research paper – that will ideally be submitted to a journal following the course.
No later than a week prior to the course, the participants should submit a synopsis with a short draft description of the data and the desired outcome. This will allow us to consider plenum lectures on some specific analysis methods

Formel requirements
Formal Requirements
We invite PhD students from all SCIENCE departments, but some experience with programming is required. The number of participants is limited to 20. If the course is overbooked, priority will be given to students who previously followed another Data Science Lab course (Introduction to Python or R, Statistical Methods I, and particularly Machine Learning and Imaging Methods).

Learning outcome
After course completion the students are expected to be able to:

- Describe the analysis methods used by others for similar problems.
- Describe relevant, alternative approaches for solving the problem.
- Develop/adapt/extend a computer-based software method for quantification and/or analysis of their own data.
- Formulate scientific questions from their PhD project in terms of research hypotheses.
- Interpret the results of their computer-based analysis in relation to their PhD project.

This depends on the individual project.
For potential background literature, see the course pages for the Introduction to Python, Introduction to R, Statistical Methods for the Biosciences I, and particularly Machine Learning and Imaging Methods

Teaching and learning methods
The course days include traditional lectures but will to a large degree be a workshop setting where the students work with their projects with the lecturers available for help, feedback, and suggestions. The participants will also have individual supervision meetings with the course lecturers. The projects must result in research article style reports and be presented before the class at the concluding examination seminar. The projects may be done using software packages of the participant’s own choice. The lecturers have particular experience with the software frameworks Python and Matlab.

Erik Dam and Raghavendra Selvan


Course workload category Hours
Course Preparation 7.00
Lectures 12.00
Theory exercises 6.00
Project work 90.00
Exam 6.00
Sum 123.00

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