In the book "All of Statistics", the author believed that Machine Learning should be called Statistical Inference in order to reveal its core thoughts. However, I've found that Machine Learning should be much more than that. It contains many thoughts in Neural Science and Clustering, besides Bayesian Inference and Hypothesis Test.
Nowadays, Machine Learning is becoming more and more popular, -- not only among scientists of Computer Science and Statistics, but almost everyone in the fields of Natural and Social Science. I don't know whether scholars in Art, Humanities or History are interested in Machine Learning. But I hope not.
I don't whether it is a good news that it becomes so popular. More and more smart persons are working on it, but I see no theoretical breakthrough hitherto. And I don't want to join this revelry.
I know that statistics can be used as a concrete tool to resolve many real-world problems perfectly. I will learn it well. But I don't believe that it can be the Messiah for science, especially for Artificial Intelligence.
My purpose of studying science is to know deeply about western philosophy. I like the feeling that I can help promote the world, but I don't want to be an engineer. I want to be a teacher -- not a teacher in high school or primary school, but a teacher of both Oriental and Western cultures and philosophies.
Thus, I cannot be crazy about Machine Learning. I will step towards the kernel of Artificial Intelligence without any stop. For ML, I regard it as a concrete tool, but not a comprehensive theory which can help me reach the philosophy hiding behind it.
