Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data? I am wondering why Machine Learning needs a lot of data compared to statistical inference. In statistics, we can use a small amount of data for a statistical inference but in Machine Learning, everybody says we need a lot of data. Why does Machine Learning needs tons of more data compared to Statistics Inference?

Machine Learning and Statistical inference deal with different type of problems and are not comparable in this point of view.
Statistical inference is used in problems that are inherently statistic, for example, if there was ten days raining then next day will more probable (using Bayesian approach) be raining as well, no need for more data.
But in machine learning, some features or patterns which exist in data must be learned. For an example in classification with machine learning, it first must learn (given a lot/enough/balanced learning data) to classify between pictures of cats and dogs, and then after learning phase, the problem in inference phase is that we show it a picture and it should tell us whether it is a cat or a dog. Now suppose that we show it 10 pictures of cats to infer and classify, and all was successfully inferred. Now, does the probability of 11th picture to be cat matter for that machine? No, because it should classify that picture based on its learned abilities to discover a cat, not the probability of being a cat after 10 cats.
