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VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
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DTSTAMP:20260608T115817Z
UID:0f9d3cd9-cde0-426e-ab1a-e075b15a31ab
DTSTART:20261012T070000Z
DTEND:20261016T150000Z
DESCRIPTION:The course provides an introduction to machine learning methods
  and workflows for life science research. It introduces the full end-to-en
 d machine learning (ML) workflow\, from data preprocessing and feature eng
 ineering to model training\, evaluation\, interpretation\, and reproducibl
 e reporting\, with a focus on the analysis of complex\, high-dimensional b
 iological data. Participants explore biological datasets using unsupervise
 d methods such as dimensionality reduction and clustering\, and build pred
 ictive models using supervised approaches including linear and tree-based 
 models. Methods for multi-omics integration\, including partial least squa
 res (PLS)\, are introduced\, and regularisation techniques for high-dimens
 ional data\, such as ridge\, lasso\, and elastic net\, are also covered.\n
 \n**Content**\n\n- Overview of the machine learning workflow\n- Dimensiona
 lity reduction methods such as PCA and UMAP\n- Unsupervised learning and c
 lustering methods\n- Supervised learning models\, including tree-based mod
 els\n- Partial least squares (PLS) for multi-omics integration\n- Regulari
 zation methods.\n- Model training\, evaluation and validation strategies\n
 - Model interpretation and explainable machine learning methods
LOCATION:SciLifeLab Uppsala\, Entrance C11\, BMC\, Husargatan 3\, Uppsala
SUMMARY:Machine Learning for Life Sciences
URL;VALUE=URI:https://uppsala.instructure.com/courses/122258
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