This idea, in fact, has been around since the mid 1960's.
http://en.wikipedia.org/wiki/Pygmalion_Effect
Well done to Duke for replicating and publicising it though, as it never seemed to percolate into practice.
Its worth noting that the major example in the wsj piece above, that of clinical trials, does not say what they think it does.
Essentially, the doctors involved do not change the results magically by taking the funding. The real explanation is far simpler -only the positive trials are released (barring FOI requests). While I accept the articles major point, the examples they choose are not good exemplars for this point.
If you have already brushed up on linear algebra and calculus (which you're gonna need if you want to do any serious ML) take a look at Hastie et al's Elements of Statistical Learning.
The PDF is free, and the book is both extremely well written and super comprehensive.
http://www-stat.stanford.edu/~tibs/ElemStatLearn/
You might also want to check out R, as its an amazing statistics language which has hundreds of packages available for ML. There's a large user community, and the really obscure error messages you get will teach you a lot about statistics. http://cran.r-project.org/
Also, a lot of machine learning is getting the data into a usable form, so learn how to use Unix command line tools such as sed, awk, grep et al. They are absolute lifesavers.
This idea, in fact, has been around since the mid 1960's. http://en.wikipedia.org/wiki/Pygmalion_Effect Well done to Duke for replicating and publicising it though, as it never seemed to percolate into practice.