Machine Learning is Optimization

It is nice to be reminded that the math part of Machine Learning is optimization.  Because that was my original training, in the form of 'Operations Research'.   Course its not all about math.  Its also about decision process, data and understanding constraints.   And formulating the right model.  See below.  Nicely put.  Its NOT just finding a magic formula.  It is establishing a complete context of the problem at hand.   O'Reilly also introduces the open software framework KeystoneML.

" ... Textbook machine learning problems can be boiled down to solving a problem in mathematical optimization, of which there are many libraries and packages to choose from. In practice, applying an algorithm is hardly the only thing a data scientist needs to do. The reality is that data scientists need to optimize data pipelines (acquire, wrangle, featurize, fit a function) which means chaining together primitives and systems and optimizing them across interdependent steps. With recent successes in computer vision, speech, and machine translation, deep neural networks provide an approach for optimizing such pipelines via gradient descent. But there’s no reason to believe that other algorithms can’t become competitive with deep learning. For alternative strategies to emerge, frameworks and platforms for comparing and optimizing data pipelines need to get better.  ... "