I was reading recently Neil Lawrence’s excellent post on how computer science degrees should be adapted given today’s challenges. Neil nicely points out that “Teaching programming alone is like teaching someone how to write without giving them something to say”, and discusses the need to understand diverse systems – unstructured documents, speech, vision, Bioinformatics etc. Then, one implicit point in Neil’s post caught my attention. Neil states:
Sitting at the core of each of these areas is machine learning: the art of processing and assimilating a range of unstructured data sources into a single model.
I found the choice of words quite interesting: A highly accomplished scientist lays claims about artistic elements in science. Is that really so? is there Art in Science??
Art involves “the expression or application of human creative skill and imagination”. It also relates to a notion of beauty and esthetics. Indeed, after spending some time in the field of ML you start seeing the beauty and creativity in elegant formulations for a specific real life problem as well as the distinct personal signatures of those formulating the solutions. Examples I recall include learning about Shannon’s information theory for the first time, the generalization of EM by Radford & Hinton , and the “magic” of boosting followed by its probabilistic interpretation by Friedman Hastie and Tibshirani with subsequent discussions . So, perhaps surprisingly similar to (yes!) Martial Arts, ML requires high technical skills but skills alone are not enough: you need to be creative in order to really push the boundary of what can be achieved and at a certain level you make the techniques your own, expressing your character.
This brings me to another important aspect of “the art in ML” which may have been alluded to in Neil’s post: What kind of models should you build? And more generally – what kind of questions you should be asking as a scientist? I’ll discuss this in my next post. In the meantime, anyone who has a nice personal example about where she/he found beauty and personal expression in ML papers is welcomed to leave it as a comment – it could make for an interesting reading list…
 A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants – Radford & Hinton 1998
 Additive logistic regression: A statistical view of boosting – Friedman Hastie and Tibshirani, 2000