Machine learning develops algorithms that use data or experience to improve performance on a specified task. The task, the experience available to the algorithm and the performance criterion must all be stated. A statistical classification system might learn from labelled training data; an unsupervised learning method instead looks for structure without those labels.[ref]
For example, a linear regression model can learn coefficients predicting a numerical outcome from measured inputs. Minimizing its error on the training data determines a fitted model, but does not establish how well it predicts new observations. A separate test set estimates performance on data withheld from fitting. Overfitting occurs when fitting peculiarities of the training data impairs this generalization; data leakage can make the evaluation misleading.[ref]
Statistical learning supplies tools for reasoning about these prediction and uncertainty questions. A useful learned model is therefore assessed through an explicit task and evaluation, alongside the running time and other resources needed to train and use it.
The main reason Ciro Santilli never touched it is that it feels that every public data set has already been fully mined or has already had the most interesting algorithms developed for it, so you can't do much outside of big companies.
This is why Ciro started Ciro's 2D reinforcement learning games to generate synthetic data and thus reduce the cost of data.
The other reason is that it is ugly.
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