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https://datalab.science.ku.dk/english/course/phd-courses/mli-methods/

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 2019

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 2019.