The Bayesians: Pick good priors and use Bayesian statistics
The Symbolists: Use top-down approaches to modeling cognition, using symbols and hand-crafted features
The Conspirators: Hinton, Lecun, Bengio et al. End-to-end deep learning without manual feature engineering
The Swiss School: Schmidhuber et al. LSTM's as a path to general AI.
The Russians: Use Support Vector Machines and its strong theoretical foundation
The Competitors: Only care about performance and generalization robustness. Not shy to build extremely slow and complex models.
The Speed Freaks: Care about fast convergence, simplicity, online learning, ease of use, scalability.
The Tree Huggers: Use mostly tree-based models, like Random Forests and Gradient Boosted Decision Trees
The Compressors: View cognition as compression. Compressed sensing, approximate matrix factorization
The Kitchen-sinkers: View learning as brute-force computation. Throw lots of feature transforms and random models and kernels at a problem
The Reinforcement learners: Look for feedback loops to add to the problem definition. The environment of the model is important.
The Complexities: Use methods and approaches from physics, dynamical systems and complexity/information theory.
The Theorists: Will not use a method, if there is no clear theory to explain it
The Pragmatists: Will use an effective method, to show that there needs to be a theory to explain it
The Cognitive Scientists: Build machine learning models to better understand (human) cognition
The Doom-sayers: ML Practitioners who worry about the singularity and care about beating human performance
The Socialists: View machine learning as a possible danger to society. Study algorithmic bias.
The Engineers: Worry about implementation, pipe-line jungles, drift, data quality.
The Combiners: Try to use the strengths of different approaches, while eliminating their weaknesses.
The Pac Learners: Search for the best hypothesis that is both accurate and computationally tractable.
See also http://www.kdnuggets.com/2015/03/all-machine-learning-models...
> It is common for people to learn about machine learning within one framework which often becomes there "home framework" through which they attempt to filter all machine learning. (Have you met people who can only think in terms of kernels? Only via Bayes Law? Only via PAC Learning?) Explicitly understanding the existence of these other frameworks can help resolve the confusion.
a) Inductive Logic Programming (and generally relational learning) is ideal for feature discovery and firmly in the symbolic camp, so the reliance of the Symbolists on hand-crafted features is not an absolute.
b) PAC learning should go under symbolic techniques, no? In fact, so should decision tree learning.
Also, I think it's obvious you can always unify and divide classifications like the above to come up with as many or as few "tribes" as you like. The real question is: are there really that many people who are wedded to their favourite technique, so much so that they won't ever try anything different?
b) if we apply hierarchical clustering, it would probably be a subset.
Anyway, this was more or less tongue-in-cheek. And yes, you could go on and on. I should have added "The Logicians", "The Game Theorists" and the NLP'ers solving object detection problems with visual bag-of-words. Also forgot to take a jab at business intelligence/operations research.
As for being wedded to a favorite technique, I think that is largely a problem for beginners (and PhD. students with a supervisor who can only think from within a certain framework). I myself may try SVM, but I rank it pretty low as an alternative.
Anyway it depends a lot on the specific algorithm, for instance see Alignment-Baed Learning  and the ADIOS algorithm  for two examples of thoroughly symbolic grammar induction algorithms (though not quite ILP) that works on unannotated, tokenised text, so is entirely unsupervised.
And, if I may be so bold, my own Masters dissertation , an unsupervised graph induction algo that learns a Prolog program from unannotated data. You won't find evidence of that on my github page, but I've used my algorithm to extract features from text- as in word embeddings. Also, it's a recursive partitioning algorithm, so essentially an unsupervised decision tree learner, only of course it learns a FOL theory rather than propositional trees. My hunch is you could use decision trees unsupervised and let them find their own features, although that'll have to go under Original Research for now :)
Those just happen to be three algorithms I know well enough, but you can google around for more examples. In general: relational learning can do away with the need for feature engineering, it's one of its big strengths.
In fact, I'm starting to think that - unless DL is somehow magical and special - it should be possible to turn most supervised learners into unsupervised feature learners, by stringing together instances of an algorithm and having each instance learn on features the previous one discovered. Again- Original Research and big pinch of salt 'cause it's just a hunch.
- statisticians (frequentist statistics, bayesian statistics; trying to find an underlying probabilistic model, even if at cost of underfitting)
- computer scientists (algorithms; ad-hoc methodology with goal of predicting data as well as possible)
- physicists (physics-motivated tools, relatively clean and composable mathematics; trying to get some properties of phenomena, even if at cost of cherry-picking "spheres in the vacuum")
"At the right vertex, we have Breiman's know-nothing approach—high-capacity models like neural nets, decision forests, and nonparametrics that will fit anything given enough data. This is engineering with less science (see these remarks). Deep learning people cluster here."
The phrase "will fit anything given enough data" is misleading and not correct about cutting-edge machine learning methods. "Fit" is a useless term, and you will instead find people talking about "bias" and "variance".
For any supervised method (you know the intended outputs) you apply to predict data, there are three sources of error: bias, variance, and random. Random error is some irreducible unpredictability that cannot be modeled. Bias occurs from bad assumptions made by the model itself (e.g. maybe the model is too simple). Variance is sensitivity to small changes in the data the algorithm is trained on.
High bias means the model is too simple to capture all the variations in the data set. High variance means the model is too overfit on the data at hand and it is not successfully generalizing to unseen data. In real-world problems there is a direct tradeoff between bias and variance. Nevertheless the goal of any supervised learning model is to have both low bias and low variance.
By splitting off a big (~10-20%) chunk of all data available into a "test" set, training the model on the remaining "train" set, then evaluating it on the "test" set, it's possible to estimate the generalizability of the model on future "unseen" data by whatever metric you want. By additionally plotting learning curves one can crudely estimate whether we have high bias or high variance.
Hence the insinuation that machine learning blindly "fits" data as much as possible is false. Sophisticated (yet not difficult) methods both minimize and estimate the generalizability of the model to future unseen data (minimizing variance), inevitably at the cost of some notion of accuracy (increasing bias).
I think the OP's objection is that such ML methods "know nothing". This is a trivial statement to make. Rather, I would turn the objection on its head and ask "If our methods achieve acceptable estimated generalizability on unseen data, do we need to know anything?". This reminds me of Alan Turing's arguments about machines passing the Turing Test vs. "are they really human?".
These high-capacity models (neural nets, decision trees, boosting) do overfit like crazy and tend to be used as black boxes without any domain knowledge. The key in his statement is when he says "given enough data," because having tons of data is one of the best ways to combat overfitting (given enough data, variance is negligible). And the fact that we can measure how much they overfit and take steps to regularize doesn't change the fact that, for example, deep learning is really way more of an engineering discipline than a mathematical or statistical discipline. And these are not criticisms of those areas at all: those are exciting areas of research precisely because there are so many unsolved problems and areas where we are working without a solid understanding!
So, with deep nets, how big a problem is getting stuck in local optima?
I mean, my intuition is that it's not magic, you're optimising a system of functions so you'd get stuck in local optima in the same way you get stuck with a single function. Is that generally the case?