Finding and classifying interesting phenomena in large image archives is often performed manually and is therefore a time- and labor-intensive process. Once a scientist has found interesting phenomena in a mission archive, they may wish to find more examples. Data science methods can streamline discovery of interesting phenomena with a high level of accuracy, dramatically reducing the time and cost of finding the relevant features while enabling interpretability. This short course will cover how data science can be used to classify features in image archives, in particular focusing on supervised learning techniques that assume a catalog of labeled image features already exist.
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