Neural network & Bayesian in this machine learning algorithm I am new to machine learning etc and found this comprehensive algorithm: http://scikit-learn.org/stable/tutorial/machine_learning_map/ . However, I am not able to make out any reference to neural networks here. Also are Bayesian methods for machine learning limited to "Naive Bayes" which is included in this graph or is it that some of the other techniques can also be done by Bayesian methods. Sincere apologies if this is a completely wrong question.
 A: I'm afraid that your question is a bit unclear. What are you asking precisely? I can only give a partial answer because I don't fully understand what you want to know.
To use Andrew Gelman's definition, Bayesian statistics refers to a statistical paradigm that makes its inferences from a posterior distribution. There are, for example, Bayesian neural networks, and Bayesian regression methods, among many other flavors of Bayesian reasoning.
I don't think that the graph is making any attempt at representing itself as an exhaustive list of machine learning methods; it's just supposed to be an illustration of the kinds of tools in scikit-learn and when you might use them. You might be a geneticist interested in genetic predisposition to disease; in that case, you'd want to use a method like disease-specific genomic analysis to preprocess your data prior to applying some other analysis method. Or perhaps you have a pile of unlablled data that you want to explore; KODAMA might be helpful.
I don't know why the diagram omits neural networks or any of the other of myriads of methods not appearing there. Certainly you can use a neural network for classification tasks; this is very common.
