An interesting reading

I came accross this interesing paper by a statistician Leo Breiman talking about the difference of statistics and machine learning. Basically, there are two school: one takes the data model, trying to understand the nature process (which is widely studied by statistician), the other one  takes an algorithmic model. They do not care the process in the black box, but just try to approximate the process using whatever effective methods such as neural network and decisions trees (this is the philosophy of machine learning guys).


Some of the discusssions are very incisive. Leo argued that the over emphasize of data model might lead to wrong conclusions. As there could be many different but comparable models to achieve the same performance. Taking a machine learning approach seems more practical.



I am more interested in reading a paper by a machine learning guy talking about statistics. 


Especially, for a newbie to work on machine learning, should he go to the statistics department or computer science department?