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There exists a rich literature of astronomical uses of AI/ML, here are some interesting papers/talks that might be relevant:. See also this set of PASP papers on machine learning in astronomy
Image and image artifact classification
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Felix Stoehr's ADASS talk on ALMA archive development "You might also like these images"
Brian Kent's demo for the 2023 June AAS splinter session, classifying VLASS sources with a neural network
Point source detection: DEEPSOURCE: point source detection using deep learning
Interferometric imaging with machine learning
https://arxiv.org/abs/2311.06349 - Interferometric Image Reconstruction using Closure Invariants and Machine Learning
Deep radio-interferometric imaging with POLISH: DSA-2000 and weak lensing (Connor et al. 2022; ADS)
First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006 (Dabbech et al. 2022; ADS); R2D2 algorithm https://arxiv.org/pdf/2403.05452
3D detection and characterization of ALMA sources through deep learning ADS Github
ESO's BRAIN study (ALMA dev. program) ArXiv
RFI detection and excision
DSC based Dual-Resunet for radio frequency interference identification
U-net based "A Self-learning Neural Network Approach for Radio Frequency Interference Detection and Removal in Radio Astronomy". Here is the Github repo
Other topics
https://www.newyorker.com/magazine/2023/11/20/a-coder-considers-the-waning-days-of-the-craft
Software resources
Scikit learn (data science scripts in python)
Tensorflow (open-source code originally from Google)
PyTorch (open source code originally from Facebook)
Kaggle (community published models and code)
Meetings/conferences
ESOGPT (September 2024)
Rare gems in big data, Tucson, May 20-23 2024
International Conference on Machine Learning for Astrophysics ML4ASTRO2
NVidia Deep Learning Institute courses