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Description
This module covers how to apply machine learning techniques to practical problems with large data-sets.听 An introduction to machine learning is presented to provide a general understanding of the concepts of machine learning and common machine learning techniques.听 Deep learning and computing frameworks to scale machine learning techniques to practical problems are then presented.听 Scientific data formats and data curation methods are also discussed.听
Specifically, the syllabus includes:听
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Foundations of machine learning (e.g. overview of ML, training, data wrangling, scikit-learn, performance analysis, gradient descent)听
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Machine learning methods (e.g. logistic regression, SVMs, ANNs, decision trees, ensemble learning and random forests, dimensionality reduction)听
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Deep learning (e.g. TensorFlow, Deep ANNs, CNNs, RNNs, Autoencoders)听
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Data formats and curation (e.g. data pipelines, data version control, databases, big-data computing) 听
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Demonstrations of ML in astrophysics, high-energy physics and industry听
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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