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This article is based on the tutorial we gave at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting. We first introduce the basic theory of machine learning and sort out the whole process of training a neural network. We then demonstrate this process with an example of inferring redshifts from SDSS spectra. To emphasize that machine learning for astronomy is easy to get started, we demonstrate that the most basic CNN network can be used to obtain high accuracy, we also show that with simple modifications, the network can be converted for classification problems and also to processing gravitational wave data., Comment: Proceedings based on the lectures given at the hands-on workshop of the ICRANet-ISFAHAN Astronomy Meeting, to be published in Astronomical and Astrophysical Transactions

The discrepancy between expected and observed cooling rates of X-ray emitting gas has led to the {\it cooling flow problem} at the cores of clusters of galaxies. A variety of models have been proposed to model the observed X-ray spectra and resolve the cooling flow problem, which involves heating the cold gas through different mechanisms. As a result, realistic models of X-ray spectra of galaxy clusters need to involve both heating {\it and} cooling mechanisms. In this paper, we argue that the heating time-scale is set by the magnetohydrodynamic (MHD) turbulent viscous heating for the Intracluster plasma, parametrised by the Shakura-Sunyaev viscosity parameter, $\alpha$. Using a cooling+heating flow model, we show that a value of $\alpha\simeq 0.05$ (with 10\% scatter) provides improved fits to the X-ray spectra of cooling flow, while at the same time, predicting reasonable cooling efficiency, $\epsilon_{cool} = 0.33^{+0.63}_{-0.15}$. Our inferred values for $\alpha$ based on X-ray spectra are also in line with direct measurements of turbulent pressure in simulations and observations of galaxy clusters. This simple picture unifies astrophysical accretion, as a balance of MHD turbulent heating and cooling, across more than 16 orders of magnitudes in scale, from neutron stars to galaxy clusters., Comment: Accepted for publication in The Astrophysical Journal