After spending plenty of time jumping back and forth between "machine learning" folks and statisticians, I finally am learning how to translate between these two communities, who do essentially identical things.
Machine Learning is a specialty of engineers, and an outgrowth of artificial intelligence research. People with machine learning expertise talk about prediction and learning using "features." This learning can be supervised or unsupervised. Machine learning folks tend to find brute-force solutions to problems with effective algorithmic optimizations. Even when Monte Carlo techniques are the enlightened machine learner's best friend, sampling methodologies don't often enter into the equation.
Statisticians are applied mathematicians, and many are methodological philosophers. Statisticians see the world in terms of central tendency and dispersion instead of hits and misses, and we use variables instead of features. We infer and predict, using regression, estimation (supervised) or clustering. While using the same basic techniques and technologies as Machine Learners, we live at the population level instead of the unit level. That's why sampling methods are so important to us, why we allocate variance, and why we lean on mathematical theorems.
The frequentist/Bayesian distinction is trivial compared to the statistician/engineer divide, at least in terms of tradition. But in the end, the tools and techniques overlap hugely. Learning both perspectives is always the best.