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Keyword
- CWL1
- FAIR1
- Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression1
- Nextflow1
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Scientific topic
- Active learning2
- Ensembl learning2
- Kernel methods2
- Knowledge representation2
- Machine learning2
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- Reinforcement learning2
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- Bioinformatics1
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- Portugal1
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Target audience
- post-docs
- Operators, developers and managers of bioinformatics resources2
- PhD students2
- Investigators and researchers working in all areas of the life sciences.1
- Mainly graduate students and Junior post-docs, of either experimental or computational background, with basic training in life sciences, or (bio)chemistry, or physics, or mathematics, or engineering1
- Master students1
- PhD Students1
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- This course is intended for master and PhD students, post-docs and staff scientists familiar with different omics data technologies who are interested in applying machine learning to analyse these data. No prior knowledge of Machine Learning concepts and methods is expected nor required1
- beginner bioinformaticians1
- bioinformaticians1
- intermediate bioinformaticians1
- students1
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