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This 5-day course is a comprehensive introduction to the principles of Machine Learning, particularly supervised and unsupervised machine learning in the context of biomedical informatics.
Students will recognise the distinctive characteristics and applicability of classification, regression and clustering to their research. The course will introduce a general framework for building Machine Learning solutions to solve a particular problem, focusing on robustness and generalizability and interpretability. A select number of algorithms will be presented in detail and those range from Tree-based classification and regression models to kernel-based methods and Neural Networks.
Hands-on practicals aim to solidify the concepts learned and provide a starting point for implementing imputation, exploration, prediction and evaluation tasks. The practicals are designed to use the Python language in 4 out of the 5 days, using Health Informatics-inspired datasets and problems. The course will have a full-day Bioinformatics session using R.
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