Anomaly detection is a challenging problem which can greatly benefit from the use of machine learning (ML) methods: unsupervised as well as semi-supervised. ML algorithms must be able to process complex, massive data sets and search for anomalies under extreme conditions (very low signal-to-noise ratio, real-time data, etc). The range of applications for anomaly detection methods is vast, and advances made in one scientific field can frequently be transferred to other disciplines, to the benefit of both parties involved.
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The 4th edition of Cosmo21 will be hosted in Chania, Greece and will gather leading experts from around the world in statistical methods and cosmology to discuss the state-of-the-art in data analysis and interpreptation. Highlights will include topics such as Bayesian techniques, machine learning, likelihood-free inference, radio data, strong and weak gravitational lensing, joint probes, and gravitational waves. The organisers also hope to attract participants to discuss other novel developments in the field.
More details can be found here.
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