The course is five full days from May 25 to May 29

The Machine Learning and Imaging Methods (MLI-M) course introduces key analysis methods in Machine Learning and Image Analysis. These allow investigations of scientific data from most fields, including data from physical measurements, questionnaires, pictures, Internet searches, and biochemical outcomes. 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 synopsis with a suggestion for an analysis ideally performed on their own data. This synopsis could form the basis for the MLI Projects PhD course offered by the Data Science Lab in September 2020.

The number of participants is limited at 60, and priority will be given to students who previously followed another Data Science Lab course (Introduction to Python or R, Statistical Methods I).
We assume that the students have some experience with Python programming.

The course is primarily for PhD students and secondarily for Post Docs from the Faculty of SCIENCE, University of Copenhagen. Post Docs and PhD Students from other faculties and universities are also allowed to sign up - but will not be admitted if we are overbooked. 

For participants not from the Faculty of SCIENCE, UCPH, there is a participation fee of 3600 kr. 

The course is five full days from May 25 to May 29.

For more practical details, see the course homepage: http://datalab.science.ku.dk/english/course/phd-courses/MLI-methods/