Graduate School of health and Medical Sciences

Introduction to machine learning in biomedical research - Part A
Provider: Faculty of Health and Medical Sciences

Activity no.: 3926-24-00-00	There are no available seats
	Enrollment deadline: 21/03/2024

Date and time	22.04.2024, at: 09:00 - 29.04.2024, at: 17:00
Regular seats	25
Course fee	9,000.00 kr.
Lecturers	Anders Krogh
ECTS credits	3.90

Contact person	Henrike Zschach E-mail address: henrike.zschach@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee. Learning objectives A student who has met the objectives of the course will be able to: 1. Understanding basic concepts in Machine Learning 2. Understand the impact of data on Machine Learning outcomes 3. Apply Machine Learning to biological and medical datasets 4. Apply Python Machine Learning modules 5. Evaluate the performance of Machine Learning system 6. Disseminate the project result in a technical report Content • Introduction to Data Analysis and Machine Learning • Data wrangling, testing methods, etch • Classic Machine Learning approaches, such as logistic regression, support vector machine, random forest • Introduction to Python for data analysis and machine learning (numpy, pandas, Scikit- learn) • Exercises in classic Machine Learning • Neural Network, including feed-forward network for biomedical data • Small group projects with real data • Groups present projects • Example applications in the biomedical field. Seminars by invited speakers. Participants PhD students enrolled at the Graduate School of Health and Medical Sciences and posdocs Prerequisites: Basic Knowledge of Python programming is required Max 25 Participants Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: - All graduate programmes Language English Form Lectures, seminars, group work, discussions, exercises, project work Course director Anders Krogh, Professor, Center for Health Data Science and Department of Computer Science, University of Copenhagen, anders.krogh@sund.ku.dk Ruth Loos, Professor, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, ruth.loos@sund.ku.dk Teachers Anders Krogh, Professor, Center for Health Data Science and Department of Computer Science, University of Copenhagen, anders.krogh@sund.ku.dk Shyam Gopalakrishnan, Associate Professor, GLOBE Institute, University of Copenhagen, shyam.gopalakrishnan@sund.ku.dk Inigo Prada Luengo Postdoc, Department of Computer Science. Inigo.luengo@bio.ku.dk Valentina Sora Postdoc, Center for Health Data Science Vaso@di.ku.dk Viktoria Shuster, PhD student, Center for Health Data Science viktoria.schuster@sund.ku.dk Dates Part A: Introduction to Machine Learning 22, 24, 25, 26 and 29 April, all days 09:00 – 17:00 Course location Room to be announced Registration Please register before 21 March 2024 Expected frequency Once a year Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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