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Machine Learning for Physicists: A Self-Study Guide
Machine Learning has become a key element in our everyday lives, especially after the garnered success of ChatGPT. But much before that, it has bing used in multiple avenues, especially where large no. of data have been involved.
In the last two decades, Physics has become data intensive. The amount of data we are getting from LHC, from both ground & space-based telescopes, from fusion reactors, etc., is huge and therefore, manually filtering them is just impossible. ML and other Data-driven techniques usually step in to segregate and make it human-reducible.
Now, if you google ML courses, then ready to be bombarded with tons of courses with no clue which one is good, and the majority of them are CS-oriented, so definitely not ideal for STEM people. On the other hand, if you are already taking a course, then finding the right information is a headache. So, here it is, a list of resources I, (training to be an Astrophysicist), personally used to train myself in Machine Learning.
Have fun!
Also, if you have any cool resources for ML, then do comment it below!
General ML understanding:
- A high-bias, low-variance introduction to Machine Learning for physicists: https://arxiv.org/abs/1803.08823 [Highly recommended]
