The added value of Machine Learning compared to traditional rule based methods I would like to showcase the value of machine learning compared to traditional rule based (symbolic AI) methods.
I would also like to break it down for Unsupervised, Supervised and Reinforcement learning. E.g.:

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*Why would you perform clustering using k-means, DBSCAN etc vs asking an expert of that field (assuming there is one) to give you some segmentation rules?

*Why would you use a Random Forest or an XGBoost to do classification rather than again getting the rules from and predictions from an expert?

*Why would you use Thompson's sampling to find the next action vs "downloading" the brain of an expert?

Ideally, I would like to move beyond the idea of "a lot of options/data/possibilities" for a human to take them all into account and process without adding their bias. Is there anything else that can answer this question without of course setting a competition between the expert and the machine?

Machine learning is fundamentally about pattern recognition. If you have sufficient data and the model is flexible enough, you can find the pattern (see universality theorems). An expert makes predictions based on their experiences (data) and some perceived relationship (pattern) in the data.
To me, ML and an expert are trying to do the same thing - with the crucial difference that human brains process the data differently and computers can crunch massive datasets whereas humans cannot. Clearly if the digitized data is a poor representation of reality or the model is inappropriate, the ML results will be worse. Alternatively humans are subject to cognitive biases and have limitations as well.
But why does it have to be exclusively one or the other? Why not embed the predictions of experts into an ML algorithm?
