Description
The module on Machine Learning and Data-driven Materials Science consists of a comprehensive exploration of the most effective data-driven techniques to solve problems in the field of regression, classification, dimension-reduction, feature extraction and clustering with particular applications to Materials Science. It provides students the essential state-of-art of machine learning techniques such as foundations of deep learning, supervised learning (on/off-line learning, linear ridge regression, neural networks, support vector machines, kernel, Bayesian Learning) unsupervised learning (clustering, granular computing, dimensionality reduction) and data feature extraction. The applications of fundamental knowledge from machine learning will be demonstrated through case studies in materials science using data from real experiments and public materials data repositories.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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